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We study the behavior of network diffusions based on the PageRank random walk from a set of seed nodes. These diffusions are known to reveal small, localized clusters (or communities) and also large macro-scale clusters by varying a parameter that has a dual-interpretation as an accuracy bound and as a regularization level. We propose a new method that quickly approximates the result of the diffusion for all values of this parameter. Our method efficiently generates an approximate $\textit{solution path}$ or $\textit{regularization path}$ associated with a PageRank diffusion, and it reveals cluster structures at multiple size-scales between small and large. We formally prove a runtime bound on this method that is independent of the size of the network, and we investigate multiple optimizations to our method that can be more practical in some settings. We demonstrate that these methods identify refined clustering structure on a number of real-world networks with up to 2 billion edges. | Seeded PageRank Solution Paths | 9,000 |
This paper details the creation and use of a massive (over 32,000 messages) artificially constructed 'Twitter' microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. PIOs searched, monitored, and (through conventional channels) verified potentially acionable information that could then be redistributed through a personalized screen name. Our case study of a key PIO reveals several capabilities that social media can support, including event detection, the distribution of information between functions within the emergency response community, and the distribution of messages to the public. We suggest that training as well as information filtering tools are necessary to realize the potential of social media in both emergencies and exercises. | On Using Synthetic Social Media Stimuli in an Emergency Preparedness
Functional Exercise | 9,001 |
Hashtags in twitter are used to track events, topics and activities. Correlated hashtag graph represents contextual relationships among these hashtags. Maximum clusters in the correlated hashtag graph can be contextually meaningful hashtag groups. In order to track the changes of the clusters and understand these hashtag groups, the hashtags in a cluster are categorized into two types: stable core and temporary members which are subject to change. Some initial studies are done in this project and 3 algorithms are designed, implemented and experimented to test them. | Stable Cluster Core Detection in Correlated Hashtag Graph | 9,002 |
The Football World Cup as world's favorite sporting event is a source of both entertainment and overwhelming amount of data about the games played. In this paper we analyse the available data on football world championships since 1930 until today. Our goal is to rank the national teams based on all matches during the championships. For this purpose, we apply the PageRank with restarts algorithm to a graph built from the games played during the tournaments. Several statistics such as matches won and goals scored are combined in different metrics that assign weights to the links in the graph. Finally, our results indicate that the Random walk approach with the use of right metrics can indeed produce relevant rankings comparable to the FIFA official all-time ranking board. | PageRank Approach to Ranking National Football Teams | 9,003 |
We consider clustering player behavior and learning the optimal team composition for multiplayer online games. The goal is to determine a set of descriptive play style groupings and learn a predictor for win/loss outcomes. The predictor takes in as input the play styles of the participants in each team; i.e., the various team compositions in a game. Our framework uses unsupervised learning to find behavior clusters, which are, in turn, used with classification algorithms to learn the outcome predictor. For our numerical experiments, we consider League of Legends, a popular team-based role-playing game developed by Riot Games. We observe the learned clusters to not only corroborate well with game knowledge, but also provide insights surprising to expert players. We also demonstrate that game outcomes can be predicted with fairly high accuracy given team composition-based features. | Player Behavior and Optimal Team Composition for Online Multiplayer
Games | 9,004 |
The ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process(SEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures the triggering effect of each forwarding, distinguishing itself from the reinforced Poisson process based model where all previous forwardings are simply aggregated as a single triggering effect. We validate the proposed model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that the SEHP model consistently outperforms the model based on reinforced Poisson process. | Modeling and Predicting Popularity Dynamics of Microblogs using
Self-Excited Hawkes Processes | 9,005 |
This study aims to investigate the COINs concept of rotating leadership within a Knowledge Building context. Individual and group level leadership patterns in a grade 4 science class were explored through temporal visualization of betweenness centrality. Results indicate that the student network was relatively decentralized, with almost all students leading the group at different points in time. Rotating leadership appears to be an emergent phenomenon of Knowledge Building, and we suggest that it has the potential to be an indicator of collective cognitive responsibility. | An Exploration of Rotating Leadership in a Knowledge Building Community | 9,006 |
Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect incidents of cyberbullying over images in Instagram, a media-based mobile social network. To this end, we have collected a sample Instagram data set consisting of images and their associated comments, and designed a labeling study for cyberbullying as well as image content using human labelers at the crowd-sourced Crowdflower Web site. An analysis of the labeled data is then presented, including a study of correlations between different features and cyberbullying as well as cyberaggression. Using the labeled data, we further design and evaluate the accuracy of a classifier to automatically detect incidents of cyberbullying. | Detection of Cyberbullying Incidents on the Instagram Social Network | 9,007 |
Predicting the popularity of items in rating networks is an interesting but challenging problem. This is especially so when an item has first appeared and has received very few ratings. In this paper, we propose a novel approach to predicting the future popularity of new items in rating networks, defining a new bipartite clustering coefficient to predict the popularity of movies and stories in the MovieLens and Digg networks respectively. We show that the clustering behaviour of the first user who rates a new item gives insight into the future popularity of that item. Our method predicts, with a success rate of over 65% for the MovieLens network and over 50% for the Digg network, the future popularity of an item. This is a major improvement on current results. | Predicting Item Popularity: Analysing Local Clustering Behaviour of
Users | 9,008 |
This paper studies the relation between activity on Twitter and sales. While research exists into the relation between Tweets and movie and book sales, this paper shows that the same relations do not hold for products that receive less attention on social media. For such products, classification of Tweets is far more important to determine a relation. Also, for such products advanced statistical relations, in addition to correlation, are required to relate Twitter activity and sales. In a case study that involves Tweets and sales from a company in four countries, the paper shows how, by classifying Tweets, such relations can be identified. In particular, the paper shows evidence that positive Tweets by persons (as opposed to companies) can be used to forecast sales and that peaks in positive Tweets by persons are strongly related to an increase in sales. These results can be used to improve sales forecasts and to increase sales in marketing campaigns. | Using Twitter to Predict Sales: A Case Study | 9,009 |
Identifying communities has always been a fundamental task in analysis of complex networks. Many methods have been devised over the last decade for detection of communities. Amongst them, the label propagation algorithm brings great scalability together with high accuracy. However, it has one major flaw; when the community structure in the network is not clear enough, it will assign every node the same label, thus detecting the whole graph as one giant community. We have addressed this issue by setting a capacity for communities, starting from a small value and gradually increasing it over time. Preliminary results show that not only our extension improves the detection capability of classic label propagation algorithm when communities are not clearly detectable, but also improves the overall quality of the identified clusters in complex networks with a clear community structure. | Controlled Label Propagation: Preventing Over-Propagation through
Gradual Expansion | 9,010 |
Nowadays, the message diffusion links among users or websites drive the development of countless innovative applications. However, in reality, it is easier for us to observe the timestamps when different nodes in the network react on a message, while the connections empowering the diffusion of the message remain hidden. This motivates recent extensive studies on the network inference problem: unveiling the edges from the records of messages disseminated through them. Existing solutions are computationally expensive, which motivates us to develop an efficient two-step general framework, Clustering Embedded Network Inference (CENI). CENI integrates clustering strategies to improve the efficiency of network inference. By clustering nodes directly on the timelines of messages, we propose two naive implementations of CENI: Infection-centric CENI and Cascade-centric CENI. Additionally, we point out the critical dimension problem of CENI: instead of one-dimensional timelines, we need to first project the nodes to an Euclidean space of certain dimension before clustering. A CENI adopting clustering method on the projected space can better preserve the structure hidden in the cascades, and generate more accurately inferred links. This insight sheds light on other related work attempting to discover or utilize the latent cluster structure in the disseminated messages. By addressing the critical dimension problem, we propose the third implementation of the CENI framework: Projection-based CENI. Through extensive experiments on two real datasets, we show that the three CENI models only need around 20% $\sim$ 50% of the running time of state-of-the-art methods. Moreover, the inferred edges of Projection-based CENI preserves or even outperforms the effectiveness of state-of-the-art methods. | CENI: a Hybrid Framework for Efficiently Inferring Information Networks | 9,011 |
Social networks help to bond people who share similar interests all over the world. As a complement, the Facebook "Like" button is an efficient tool that bonds people with the online information. People click on the "Like" button to express their fondness of a particular piece of information and in turn tend to visit webpages with high "Like" count. The important fact of the Like count is that it reflects the number of actual users who "liked" this information. However, according to our study, one can easily exploit the defects of the "Like" button to counterfeit a high "Like" count. We provide a proof-of-concept implementation of these exploits, and manage to generate 100 fake Likes in 5 minutes with a single account. We also reveal existing counterfeiting techniques used by some online sellers to achieve unfair advantage for promoting their products. To address this fake Like problem, we study the varying patterns of Like count and propose an innovative fake Like detection method based on clustering. To evaluate the effectiveness of our algorithm, we collect the Like count history of more than 9,000 websites. Our experiments successfully uncover 16 suspicious fake Like buyers that show abnormal Like count increase patterns. | Does "Like" Really Mean Like? A Study of the Facebook Fake Like
Phenomenon and an Efficient Countermeasure | 9,012 |
A hybrid social network for building social communities for a university community is presented. The system employed the semantic ontology for an offline/online social network site (SNS). It captures the core features of an SNS including profile creation, friend invite/search, group formation, chatting/messaging, blogging and voting. Three core frameworks - the peer2me framework, SMSN semantic mobile social network and Peoplepods framework were considered in the implementation phase. The results show remarkable matching performance for prosumers with similar interests with relevance close to unity. The social network was able to capture the needs of the university students by serving as a handy direction to popular locations within the campus. | Hybrid Social Networking Application for a University Community | 9,013 |
During recent years the online social networks (in particular Twitter) have become an important alternative information channel to traditional media during natural disasters, but the amount and diversity of messages poses the challenge of information overload to end users. The goal of our research is to develop an automatic classifier of tweets to feed a mobile application that reduces the difficulties that citizens face to get relevant information during natural disasters. In this paper, we present in detail the process to build a classifier that filters tweets relevant and non-relevant to an earthquake. By using a dataset from the Chilean earthquake of 2010, we first build and validate a ground truth, and then we contribute by presenting in detail the effect of class imbalance and dimensionality reduction over 5 classifiers. We show how the performance of these models is affected by these variables, providing important considerations at the moment of building these systems. | Identifying Relevant Messages in a Twitter-based Citizen Channel for
Natural Disaster Situations | 9,014 |
This paper is an extensive survey of literature on complex network communities and clustering. Complex networks describe a widespread variety of systems in nature and society especially systems composed by a large number of highly interconnected dynamical entities. Complex networks like real networks can also have community structure. There are several types of methods and algorithms for detection and identification of communities in complex networks. Several complex networks have the property of clustering or network transitivity. Some of the important concepts in the field of complex networks are small-world and scale-robustness, degree distributions, clustering, network correlations, random graph models, models of network growth, dynamical processes on networks, etc. Some current areas of research on complex network communities are those on community evolution, overlapping communities, communities in directed networks, community characterization and interpretation, etc. Many of the algorithms or methods proposed for network community detection through clustering are modified versions of or inspired from the concepts of minimum-cut based algorithms, hierarchical connectivity based algorithms, the original GirvanNewman algorithm, concepts of modularity maximization, algorithms utilizing metrics from information and coding theory, and clique based algorithms. | Complex Networks, Communities and Clustering: A survey | 9,015 |
We study student behavior and performance in two Massive Open Online Courses (MOOCs). In doing so, we present two frameworks by which video-watching clickstreams can be represented: one based on the sequence of events created, and another on the sequence of positions visited. With the event-based framework, we extract recurring subsequences of student behavior, which contain fundamental characteris- tics such as reflecting (i.e., repeatedly playing and pausing) and revising (i.e., plays and skip backs). We find that some of these behaviors are significantly associated with whether a user will be Correct on First Attempt (CFA) or not in answering quiz questions. With the position-based framework, we then devise models for performance. In evaluating these through CFA prediction, we find that three of them can substantially improve prediction quality in terms of accuracy and F1, which underlines the ability to relate behavior to performance. Since our prediction considers videos individually, these benefits also suggest that our models are useful in situations where there is limited training data, e.g., for early detection or in short courses. | Mining MOOC Clickstreams: On the Relationship Between Learner Behavior
and Performance | 9,016 |
The focus of this paper is modeling what we call a Social Radar, i.e. a method to estimate the relative influence between social agents, by sampling their opinions and as they evolve, after injecting in the network stubborn agents. The stubborn agents opinion is not influenced by the peers they seek to sway, and their opinion bias is the known input to the social network system. The novelty is in the model presented to probe a social network and the solution of the associated regression problem. The model allows to map the observed opinion onto system equations that can be used to infer the social graph and the amount of trust that characterizes the links. | The Social System Identification Problem | 9,017 |
Crisis informetrics is considered to be a relatively new and emerging area of research, which deals with the application of analytical approaches of network and information science combined with experimental learning approaches of statistical mechanics to explore communication and information flow, robustness as well as tolerance of complex crisis networks under threats. In this paper, we discuss the scale free network property of an organizational communication network and test both traditional (static) and dynamic topology of social networks during organizational crises Both types of topologies exhibit similar characteristics of prominent actors reinforcing the power law distribution nature of scale free networks. There are no significant fluctuations among the actor prominence in daily and aggregated networks. We found that email communication network display a high degree of scale free behavior described by power law. | Statistical Network Topology for Crisis Informetrics | 9,018 |
Twitter is an extremely popular social networking platform. Most Twitter users do not disclose their locations due to privacy concerns. Although inferring the location of an individual Twitter user has been extensively studied, it is still missing to effectively find the majority of the users in a specific geographical area without scanning the whole Twittersphere, and obtaining these users will result in both positive and negative significance. In this paper, we propose LocInfer, a novel and lightweight system to tackle this problem. LocInfer explores the fact that user communications in Twitter exhibit strong geographic locality, which we validate through large-scale datasets. Based on the experiments from four representative metropolitan areas in U.S., LocInfer can discover on average 86.6% of the users with 73.2% accuracy in each area by only checking a small set of candidate users. We also present a countermeasure to the users highly sensitive to location privacy and show its efficacy by simulations. | Your Actions Tell Where You Are: Uncovering Twitter Users in a
Metropolitan Area | 9,019 |
Recent decades have witnessed online social media being a big-data window for quantificationally testifying conventional social theories and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden "ambivalent users" are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights or at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and thus serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift of words adopted in ambivalent tweets is more evident than usual and exhibits a clear "negative to positive" pattern. The above observations, though being promiscuous seemingly, actually point to the self regulation of negative mood in Weibo, which could find its base from the emotion management theories in sociology but makes an interesting extension to the online environment. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing. | Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual
Behaviors in Weibo | 9,020 |
The node set of a two-mode network consists of two disjoint subsets and all its links are linking these two subsets. The links can be weighted. We developed a new method for identifying important sub-networks in two-mode networks. The method combines and extends the ideas from generalized cores in one-mode networks and from (p, q)- cores for two-mode networks. In this paper we introduce the notion of generalized two-mode cores and discuss some of their properties. An efficient algorithm to determine generalized two-mode cores and an analysis of its complexity are also presented. For illustration some results obtained in analyses of real-life data are presented. | Generalized Two-mode Cores | 9,021 |
From its origins in the mid 90s, the application of the concept of virality to commercial communication has represented an opportunity for brands to cross the traditional barriers of the audience concerning advertising and turn it into active communicator of brand messages. Viral marketing is based, since then, on two basic principles: offer free and engaging content that mask its commercial purpose to the individual and using a peer-to-peer dissemination system. The transformation of the passive spectator into an active user who broadcasts advertising messages promoted by sponsors, responds to needs and motivations of individuals and content features which have been described by previous research in this field, mainly through quantitative methods based on user perceptions. This paper focusses on those elements detected in its previous research as promoters of the sharing action in the 25 most shared viral video ads between 2006 and 2013 using content analysis. The results obtained show the most common features in these videos and the prominent presence of surprise and joy as dominant emotions in the most successful viral videos. | The Key Elements of Viral Advertising. From Motivation to Emotion in the
Most Shared Videos | 9,022 |
The uniqueness of online social networks makes it possible to implement new methods that increase the quality and effectiveness of research processes. While surveys are one of the most important tools for research, the representativeness of selected online samples is often a challenge and the results are hardly generalizable. An approach based on surveys with representativeness targeted at network measure distributions is proposed and analysed in this paper. Its main goal is to focus not only on sample representativeness in terms of demographic attributes, but also to follow the measures distributions within main network. The approach presented has many application areas related to online research, sampling a network for the evaluation of collaborative learning processes, and candidate selection for training purposes with the ability to distribute information within a social network. | Knowledge Acquisition from Social Platforms Based on Network
Distributions Fitting | 9,023 |
Online Social Networking is a fascinating phenomena, attracting more than one billion people. It supports basic human needs such as communication, socializing with others and reputation building. Thus, an in-depth understanding of user behavior in Online Social Networks (OSNs) can provide major insights into human behavior, and impacts design choices of social platforms and applications. However, researchers have only limited access to behavioral data. As a consequence of this limitation, user behavior in OSNs as well as its development in recent years are still not deeply understood. In this paper, we present a study about user behavior on the most popular OSN, Facebook, with 2071 participants from 46 countries. We elaborate how Facebookers orchestrate the offered functions to achieve individual benefit in 2014 and evaluate user activity changes from 2009 till 2014 to understand the development of user behavior. Inter alia, we focus on the most important functionality, the newsfeed, to understand content sharing amongst users. We (i) yield a better understanding on content sharing and consumption and (ii) refine behavioral assumptions in the literature to improve the performance of alternative social platforms. Furthermore, we (iii) contribute evidence to the discussion of Facebook to be an aging network. | The User Behavior in Facebook and its Development from 2009 until 2014 | 9,024 |
The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (i.e. mention-anomaly-based event detection), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users' interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events. | Event detection, tracking, and visualization in Twitter: a
mention-anomaly-based approach | 9,025 |
Privacy in Online Social Networks (OSNs) evolved from a niche topic to a broadly discussed issue in a wide variety of media. Nevertheless, OSNs drastically increase the amount of information that can be found about individuals on the web. To estimate the dimension of data leakage in OSNs, we measure the real exposure of user content of 4,182 Facebook users from 102 countries in the most popular OSN, Facebook. We further quantify the impact of a comprehensible privacy control interface that has been shown to extremely decrease configuration efforts as well as misconfiguration in audience selection. Our study highlights the importance of usable security. (i) The total amount of content that is visible to Facebook users does not dramatically decrease by simplifying the audience selection interface, but the composition of the visible content changes. (ii) Which information is uploaded to Facebook as well as which information is shared with whom strongly depends on the user's country of origin. | Private Date Exposure in Facebook and the Impact of Comprehensible
Audience Selection Controls | 9,026 |
Recently the influence maximization problem has received much attention for its applications on viral marketing and product promotions. However, such influence maximization problems have not taken into account the monetary effect on the purchasing decision of individuals. To fulfill this gap, in this paper, we aim for maximizing the revenue by considering the quantity constraint on the promoted commodity. For this problem, we not only identify a proper small group of individuals as seeds for promotion but also determine the pricing of the commodity. To tackle the revenue maximization problem, we first introduce a strategic searching algorithm, referred to as Algorithm PRUB, which is able to derive the optimal solutions. After that, we further modify PRUB to propose a heuristic, Algorithm PRUB+IF, for obtaining feasible solutions more effciently on larger instances. Experiments on real social networks with different valuation distributions demonstrate the effectiveness of PRUB and PRUB+IF. | An Effective Marketing Strategy for Revenue Maximization with a Quantity
Constraint | 9,027 |
What if a successful company starts to receive a torrent of low-valued (one or two stars) recommendations in its mobile apps from multiple users within a short (say one month) period of time? Is it legitimate evidence that the apps have lost in quality, or an intentional plan (via lockstep behavior) to steal market share through defamation? In the case of a systematic attack to one's reputation, it might not be possible to manually discern between legitimate and fraudulent interaction within the huge universe of possibilities of user-product recommendation. Previous works have focused on this issue, but none of them took into account the context, modeling, and scale that we consider in this paper. Here, we propose the novel method Online-Recommendation Fraud ExcLuder (ORFEL) to detect defamation and/or illegitimate promotion of online products by using vertex-centric asynchronous parallel processing of bipartite (users-products) graphs. With an innovative algorithm, our results demonstrate both efficacy and efficiency -- over 95% of potential attacks were detected, and ORFEL was at least two orders of magnitude faster than the state-of-the-art. Over a novel methodology, our main contributions are: (1) a new algorithmic solution; (2) one scalable approach; and (3) a novel context and modeling of the problem, which now addresses both defamation and illegitimate promotion. Our work deals with relevant issues of the Web 2.0, potentially augmenting the credibility of online recommendation to prevent losses to both customers and vendors. | ORFEL: efficient detection of defamation or illegitimate promotion in
online recommendation | 9,028 |
Blogging is a popular way of expressing opinions and discussing topics. Bloggers demonstrate different levels of commitment and most interesting are influential bloggers. Around such bloggers, the groups are forming, which concentrate users sharing similar interests. Finding such bloggers is an important task and has many applications e.g. marketing, business, politics. Influential ones affect others which is related to the process of diffusion. However, there is no objective way to telling which blogger is more influential. Therefore, researchers take into consideration different criteria to assess bloggers (e.g. SNA centrality measures). In this paper we propose new, efficient method for influential bloggers discovery which is based on relation of commenting in blogger's thread and is defined on bloggers level. Next, we compare results with other, comparative method proposed by Agarwal et al. called iFinder which is based on links between posts. | Finding Influential Bloggers | 9,029 |
Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally. | Large Graph Analysis in the GMine System | 9,030 |
On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities are found in the messages of two different accounts, the more similar, in terms of content or interest, they tend to be. Towards this direction, we introduce a methodology for discovering and suggesting similar Twitter accounts, based entirely on their disseminated content in terms of Twitter entities used. The methodology is based exclusively on semantic representation protocols and related technologies. An ontological schema is also described towards the semantification of the Twitter accounts and their entities. | Discovering similar Twitter accounts using semantics | 9,031 |
This paper proposes a new prediction process to explain and predict popularity evolution of YouTube videos. We exploit our recent study on the classification of YouTube videos in order to predict the evolution of videos' view-count. This classification allows to identify important factors of the observed popularity dynamics. Our experimental results show that our prediction process is able to reduce the average prediction errors compared to a state-of-the-art baseline model. We also evaluate the impact of adding popularity criteria in our classification. | Forecasting popularity of videos in YouTube | 9,032 |
For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network dynamics and message flow cascades, the problem of recommending personalized posting times has remained an underexplored topic of research. In this study, we formulate a when-to-post problem, where the objective is to find the best times for a user to post on social networks in order to maximize the probability of audience responses. To understand the complexity of the problem, we examine user behavior in terms of post-to-reaction times, and compare cross-network and cross-city weekly reaction behavior for users in different cities, on both Twitter and Facebook. We perform this analysis on over a billion posted messages and observed reactions, and propose multiple approaches for generating personalized posting schedules. We empirically assess these schedules on a sampled user set of 0.5 million active users and more than 25 million messages observed over a 56 day period. We show that users see a reaction gain of up to 17% on Facebook and 4% on Twitter when the recommended posting times are used. We open the dataset used in this study, which includes timestamps for over 144 million posts and over 1.1 billion reactions. The personalized schedules derived here are used in a fully deployed production system to recommend posting times for millions of users every day. | When-To-Post on Social Networks | 9,033 |
Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has proposed methods to match profiles and showed that it is possible to do it accurately, but using evaluations that focused on sampled datasets only. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks, by exploiting public attributes, i.e., information users publicly provide about themselves. Today's social networks have hundreds of millions of users, which brings completely new challenges as a reliable matching scheme must identify the correct matching profile out of the millions of possible profiles. We first define a set of properties for profile attributes--Availability, Consistency, non-Impersonability, and Discriminability (ACID)--that are both necessary and sufficient to determine the reliability of a matching scheme. Using these properties, we propose a method to evaluate the accuracy of matching schemes in real practical cases. Our results show that the accuracy in practice is significantly lower than the one reported in prior literature. When considering entire social networks, there is a non-negligible number of profiles that belong to different users but have similar attributes, which leads to many false matches. Our paper sheds light on the limits of matching profiles in the real world and illustrates the correct methodology to evaluate matching schemes in realistic scenarios. | On the Reliability of Profile Matching Across Large Online Social
Networks | 9,034 |
Distances in a network capture relations between nodes and are the basis of centrality, similarity, and influence measures. Often, however, the relevance of a node $u$ to a node $v$ is more precisely measured not by the magnitude of the distance, but by the number of nodes that are closer to $v$ than $u$. That is, by the {\em rank} of $u$ in an ordering of nodes by increasing distance from $v$. We identify and address fundamental challenges in rank-based graph mining. We first consider single-source computation of reverse-ranks and design a "Dijkstra-like" algorithm which computes nodes in order of increasing approximate reverse rank while only traversing edges adjacent to returned nodes. We then define {\em reverse-rank influence}, which naturally extends reverse nearest neighbors influence [Korn and Muthukrishnan 2000] and builds on a well studied distance-based influence. We present near-linear algorithms for greedy approximate reverse-rank influence maximization. The design relies on our single-source algorithm. Our algorithms utilize near-linear preprocessing of the network to compute all-distance sketches. As a contribution of independent interest, we present a novel algorithm for computing these sketches, which have many other applications, on multi-core architectures. We complement our algorithms by establishing the hardness of computing {\em exact} reverse-ranks for a single source and {\em exact} reverse-rank influence. This implies that when using near-linear algorithms, the small relative errors we obtain are the best we can currently hope for. Finally, we conduct an experimental evaluation on graphs with tens of millions of edges, demonstrating both scalability and accuracy. | Reverse Ranking by Graph Structure: Model and Scalable Algorithms | 9,035 |
The value of a social network is generally determined by its size and the connectivity of its nodes. But since some of the nodes may be fake ones and others that are dormant, the question of validating the node counts by statistical tests becomes important. In this paper we propose the use of the Benford's distribution to check on the trustworthiness of the connectivity statistics. Our experiments using statistics of both symmetric and asymmetric networks show that when the accumulation processes are random, the convergence to Benford's law is significantly better, and therefore this fact can be used to distinguish between processes which are randomly generated and those with internal dependencies. | Connectivity in Social Networks | 9,036 |
Several graph visualization tools exist. However, they are not able to handle large graphs, and/or they do not allow interaction. We are interested on large graphs, with hundreds of thousands of nodes. Such graphs bring two challenges: the first one is that any straightforward interactive manipulation will be prohibitively slow. The second one is sensory overload: even if we could plot and replot the graph quickly, the user would be overwhelmed with the vast volume of information because the screen would be too cluttered as nodes and edges overlap each other. GMine system addresses both these issues, by using summarization and multi-resolution. GMine offers multi-resolution graph exploration by partitioning a given graph into a hierarchy of com-munities-within-communities and storing it into a novel R-tree-like structure which we name G-Tree. GMine offers summarization by implementing an innovative subgraph extraction algorithm and then visualizing its output. | GMine: A System for Scalable, Interactive Graph Visualization and Mining | 9,037 |
To enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. The shared users between different networks are called anchor users, while the remaining unshared users are named as non-anchor users. Connections between accounts of anchor users in different networks are defined as anchor links and networks partially aligned by anchor links can be represented as partially aligned networks. In this paper, we want to predict anchor links between partially aligned social networks, which is formally defined as the partial network alignment problem. The partial network alignment problem is very difficult to solve because of the following two challenges: (1) the lack of general features for anchor links, and (2) the "one-to-one$_\le$" (one to at most one) constraint on anchor links. To address these two challenges, a new method PNA (Partial Network Aligner) is proposed in this paper. PNA (1) extracts a set of explicit anchor adjacency features and latent topological features for anchor links based on the anchor meta path concept and tensor decomposition techniques, and (2) utilizes the generic stable matching to identify the non-anchor users to prune the redundant anchor links attached to them. Extensive experiments conducted on two real-world partially aligned social networks demonstrate that PNA can solve the partial network alignment problem very well and outperform all the other comparison methods with significant advantages. | Partial Network Alignment with Anchor Meta Path and Truncated Generic
Stable Matching | 9,038 |
In this report there will be a discussion for Information Diffusion. There will be discussions on what information diffusion is, its key characteristics and on several other aspects of these kinds of networks. This report will focus on peer to peer models in information diffusion. There will be discussions on epidemic model, OSN and other details related to information diffusion. | Information Diffusion issues | 9,039 |
For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed as influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the static social networks. In fact, due to the high speed data transmission and the large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. Unfortunately, as shown in the experiments, in such cases the state-of-art seeding strategies are pessimistic as they fails to trace the dynamic changes in a social network. In this paper, we study the strategies selecting seed users in an adaptive manner. We first formally model the Dynamic Independent Cascade model and introduce the concept of adaptive seeding strategy. Then based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm an efficient heuristic algorithm is provided in order to meet practical requirements. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other standard methods. | Adaptive Influence Maximization in Dynamic Social Networks | 9,040 |
When nodes can repeatedly update their behavior (as in agent-based models from computational social science or repeated-game play settings) the problem of optimal network seeding becomes very complex. For a popular spreading-phenomena model of binary-behavior updating based on thresholds of adoption among neighbors, we consider several planning problems in the design of \textit{Sticky Interventions}: when adoption decisions are reversible, the planner aims to find a Seed Set where temporary intervention leads to long-term behavior change. We prove that completely converting a network at minimum cost is $\Omega(\ln (OPT) )$-hard to approximate and that maximizing conversion subject to a budget is $(1-\frac{1}{e})$-hard to approximate. Optimization heuristics which rely on many objective function evaluations may still be practical, particularly in relatively-sparse networks: we prove that the long-term impact of a Seed Set can be evaluated in $O(|E|^2)$ operations. For a more descriptive model variant in which some neighbors may be more influential than others, we show that under integer edge weights from $\{0,1,2,...,k\}$ objective function evaluation requires only $O(k|E|^2)$ operations. These operation bounds are based on improvements we give for bounds on time-steps-to-convergence under discrete-time reversible-threshold updates in networks. | Sticky Seeding in Discrete-Time Reversible-Threshold Networks | 9,041 |
One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method able to collect and summarize data. Sampling the content of an Online Social Network (OSN) plays an important role as a knowledge discovery tool. It is becoming increasingly difficult to ignore the fact that current sampling methods must cope with a lack of a full sampling frame i.e., there is an imposed condition determined by a limited data access. In addition, another key aspect to take into account is the huge amount of data generated by users of social networking services. This type of conditions make especially difficult to develop sampling methods to collect truly reliable data. Therefore, we propose a low computational cost method for sampling emerging global trends on social networking services such as Twitter. The main purpose of this study, is to develop a methodology able to carry out an efficient collecting process via three random generators: Brownian, Illusion and Reservoir. These random generators will be combined with a Metropolis-Hastings Random Walk (MHRW) in order to improve the sampling process. We demonstrate the effectiveness of our approach by correctly providing a descriptive statistics of the collected data. In addition, we also sketch the collecting procedure on real-time carried out on Twitter. Finally, we conclude with a trend concentration graphical description and a formal convergence analysis to evaluate whether the sample of draws has attained an equilibrium state to get a rough estimate of the sample quality. | Towards a Standard Sampling Methodology on Online Social Networks:
Collecting Global Trends on Twitter | 9,042 |
It's of great importance to measure happiness of social network users, but the existing method based on questionnaires suffers from high costs and low efficiency. This paper aims at identifying social network users' happiness level based on their Web behavior. We recruited 548 participants to fill in the Oxford Happiness Inventory (OHI) and divided them into two groups with high/low OHI score. We downloaded each Weibo user's data by calling API, and extracted 103 linguistic and behavior features. 24 features are identified with significant difference between high and low happiness groups. We trained a Decision Tree on these 24 features to make the prediction of high/low happiness group. The decision tree can be used to identify happiness level of any new social network user based on linguistic and behavior features. The Decision Tree can achieve 67.7% on precision. Although the capability of our Decision Tree is not ideal, classifying happiness via linguistic and behavior features on the Internet is proved to be feasible. | Classify Sina Weibo users into High or Low happiness Groups Using
Linguistic and Behavior Features | 9,043 |
This paper presents methods to compare high order networks, defined as weighted complete hypergraphs collecting relationship functions between elements of tuples. They can be considered as generalizations of conventional networks where only relationship functions between pairs are defined. Important properties between relationships of tuples of different lengths are established, particularly when relationships encode dissimilarities or proximities between nodes. Two families of distances are then introduced in the space of high order networks. The distances measure differences between networks. We prove that they are valid metrics in the spaces of high order dissimilarity and proximity networks modulo permutation isomorphisms. Practical implications are explored by comparing the coauthorship networks of two popular signal processing researchers. The metrics succeed in identifying their respective collaboration patterns. | Metrics in the Space of High Order Networks | 9,044 |
Studies on friendships in online social networks involving geographic distance have so far relied on the city location provided in users' profiles. Consequently, most of the research on friendships have provided accuracy at the city level, at best, to designate a user's location. This study analyzes a Twitter dataset because it provides the exact geographic distance between corresponding users. We start by introducing a strong definition of "friend" on Twitter (i.e., a definition of bidirectional friendship), requiring bidirectional communication. Next, we utilize geo-tagged mentions delivered by users to determine their locations, where "@username" is contained anywhere in the body of tweets. To provide analysis results, we first introduce a friend counting algorithm. From the fact that Twitter users are likely to post consecutive tweets in the static mode, we also introduce a two-stage distance estimation algorithm. As the first of our main contributions, we verify that the number of friends of a particular Twitter user follows a well-known power-law distribution (i.e., a Zipf's distribution or a Pareto distribution). Our study also provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides concrete evidence that Twitter can be a useful platform for assigning a more accurate scalar value to the degree of friendship between two users. | A New Understanding of Friendships in Space: Complex Networks Meet
Twitter | 9,045 |
Online social networking (OSN) has become of great influence to Filipinos, where Facebook, Twitter, LinkedIn, Google+, and Instagram are among the popular ones. Their popularity, coupled with their intuitive and interactive use, allow one's personal information such as gender, age, address, relationship status, and list of friends to become publicly available. The accessibility of information from these sites allow, with the aid of computers, for the study of a wide population's characteristics even in a provincial scale. Aside from being neighbouring locales, the respective residents of Laguna and Batangas both derive their livelihoods from two lakes, Laguna de Bay and Taal Lake. Both residents experience similar problems, such as that, among many others, of fish kill. The goal of this research is to find out similarities in their respective online populations, particularly that of Facebook's. With the use of computational dynamic social network analysis (CDSNA), we found out that the two communities are similar, among others, as follows: o Both populations are dominated by single young female o Homophily was observed when choosing a friend in terms of age (i.e., friendships were created more often between people whose ages do not differ by at most five years); and o Heterophily was observed when choosing friends in terms of gender (i.e., more friendships were created between a male and a female than between both people of the same gender). This paper also presents the differences in the structure of the two social networks, such as degrees of separation and preferential attachment. | Ang Social Network sa Facebook ng mga Taga-Batangas at ng mga
Taga-Laguna: Isang Paghahambing | 9,046 |
Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags. | Overcoming data scarcity of Twitter: using tweets as bootstrap with
application to autism-related topic content analysis | 9,047 |
High order networks are weighted hypergraphs col- lecting relationships between elements of tuples, not necessarily pairs. Valid metric distances between high order networks have been defined but they are difficult to compute when the number of nodes is large. The goal here is to find tractable approximations of these network distances. The paper does so by mapping high order networks to filtrations of simplicial complexes and showing that the distance between networks can be lower bounded by the difference between the homological features of their respective filtrations. Practical implications are explored by classifying weighted pairwise networks constructed from different gener- ative processes and by comparing the coauthorship networks of engineering and mathematics academic journals. The persistent homology methods succeed in identifying different generative models, in discriminating engineering and mathematics commu- nities, as well as in differentiating engineering communities with different research interests. | Persistent Homology Lower Bounds on High Order Network Distances | 9,048 |
This paper proposes and investigates a Boolean gossip model as a simplified but non-trivial probabilistic Boolean network. With positive node interactions, in view of standard theories from Markov chains, we prove that the node states asymptotically converge to an agreement at a binary random variable, whose distribution is characterized for large-scale networks by mean-field approximation. Using combinatorial analysis, we also successfully count the number of communication classes of the positive Boolean network explicitly in terms of the topology of the underlying interaction graph, where remarkably minor variation in local structures can drastically change the number of network communication classes. With general Boolean interaction rules, emergence of absorbing network Boolean dynamics is shown to be determined by the network structure with necessary and sufficient conditions established regarding when the Boolean gossip process defines absorbing Markov chains. Particularly, it is shown that for the majority of the Boolean interaction rules, except for nine out of the total $2^{16}-1$ possible nonempty sets of binary Boolean functions, whether the induced chain is absorbing has nothing to do with the topology of the underlying interaction graph, as long as connectivity is assumed. These results illustrate possibilities of {relating dynamical} properties of Boolean networks to graphical properties of the underlying interactions. | Boolean Gossip Networks | 9,049 |
In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of $(1-1/e-\epsilon)^3$ for any (polynomially small) $\epsilon > 0$. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function. | Combining Traditional Marketing and Viral Marketing with Amphibious
Influence Maximization | 9,050 |
We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs - Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks. | #mytweet via Instagram: Exploring User Behaviour across Multiple Social
Networks | 9,051 |
Chat-log data that contains information about sender and receiver of the statements sent around in the chat can be readily turned into a directed temporal multi-network representation. In the resulting network, the activity of a chat member can, for example, be operationalized as his degree (number of distinct interaction partners) or his strength (total number of interactions). However, the data itself contains more information that is not readily representable in the network, e.g., the total number of words used by a member or the reaction time to what the members said. As degree and strength, these values can be seen as a way to operationalize the idea of activity of a chat-log member. This paper deals with the question of how the overall activity of a member can be assessed, given multiple and probably opposing criteria by using a fuzzy operator. We then present a new way of visualizing the results and show how to apply it to the network representation of chat-log data. Finally, we discuss how this approach can be used to deal with other conflicting situations, like the different rankings produced by different centrality indices. | Analyzing the activity of a person in a chat by combining network
analysis and fuzzy logic | 9,052 |
Items shared through Social Media may affect more than one user's privacy --- e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users' privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users' would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users' behaviour. | Resolving Multi-party Privacy Conflicts in Social Media | 9,053 |
Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the filtering and aggregation of social media content for disseminating news stories. In this paper, we perform a systematic methodological study of controversy detection by using the content and the network structure of social media. Unlike previous work, rather than study controversy in a single hand-picked topic and use domain specific knowledge, we take a general approach to study topics in any domain. Our approach to quantifying controversy is based on a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph. We perform an extensive comparison of controversy measures, different graph-building approaches, and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task. | Quantifying Controversy in Social Media | 9,054 |
Fish kill events (FKE) in the caldera lake of Taal occur rarely (only 0.5\% in the last 10 years) but each event has a long-term effect on the environmental health of the lake ecosystem, as well as a devastating effect on the financial and emotional aspects of the residents whose livelihood rely on aquaculture farming. Predicting with high accuracy when within seven days and where on the vast expanse of the lake will FKEs strike will be a very important early warning tool for the lake's aquaculture industry. Mathematical models to predict the occurrences of FKEs developed by several studies done in the past use as predictors the physico-chemical characteristics of the lake water, as well as the meteorological parameters above it. Some of the models, however, did not provide acceptable predictive accuracy and enough early warning because they were developed with unbalanced binary data set, i.e., characterized by dense negative examples (no FKE) and highly sparse positive examples (with FKE). Other models require setting up an expensive sensor network to measure the water parameters not only at the surface but also at several depths. Presented in this paper is a system for capturing, measuring, and visualizing the contextual sentiment polarity (CSP) of dated and geolocated social media microposts of residents within 10km radius of the Taal Volcano crater ($14^\circ$N, $121^\circ$E). High frequency negative CSP co-occur with FKE for two occasions making human expressions a viable non-physical sensors for impending FKE to augment existing mathematical models. | A System for Sensing Human Sentiments to Augment a Model for Predicting
Rare Lake Events | 9,055 |
Sociological studies on transnational migration are often based on surveys or interviews, an expensive and time consuming approach. On the other hand, the pervasiveness of mobile phones and location aware social networks has introduced new ways to understand human mobility patterns at a national or global scale. In this work, we leverage geo located information obtained from Twitter as to understand transnational migration patterns between two border cities (San Diego, USA and Tijuana, Mexico). We obtained 10.9 million geo located tweets from December 2013 to January 2015. Our method infers human mobility by inspecting tweet submissions and user's home locations. Our results depict a trans national community structure that exhibits the formation of a functional metropolitan area that physically transcends international borders. These results show the potential for re analysing sociology phenomena from a technology based empirical perspective. | Tweeting Over The Border: An Empirical Study of Transnational Migration
in San Diego and Tijuana | 9,056 |
In this paper, we study a novel spatial crowdsourcing system where the workers' time availabilities and their spatial locations are known a priori. Consequently, the tasks assignment to workers is performed not only based on the current location of the human workers and the tasks available in the region, but also based on the availability of the workers during the specific times that a given task should be accepted, processed, and completed. Having the system determine the daily pattern of the workers (either by predefined questionnaires when the workers register, or by archiving data from the worker's mobile devices, or by on the road and real-time entered status data) eliminates many unsuccessful task assignments and therefore significantly increases the efficiency of the system. In the original Spatial Crowdsourcing (SC) framework, the SC-server optimizes the task assignment locally at every instance of time and whenever a new task, or a new worker, enters the system. Our new framework (PSC), on the other hand, allows the users to enter their daily routine, and temporal, spatial, and availability patterns a priori. This makes the system much more stable and pattern-opportunistic. The PSC servers can focus on receiving and archiving new entries (e.g., workers, tasks, and their criteria) during busy times (e.g., when there are many new entries in the system), and can focus on optimization and computations during quiet times (e.g., when there are fewer new entries in the system). Having the task optimization process happen during quiet times, and when there are few changes to the system, makes the performance more stable and reliable. It also allows the PSC system to have a global view of the system and and perform global optimizations to improve the performance. | PSC: A Pattern-Based Temporal and Spatial Crowdsourcing Platform to
Improve Performance, Reliability, and Privacy | 9,057 |
This paper introduces LABurst, a general technique for identifying key moments, or moments of high impact, in social media streams without the need for domain-specific information or seed keywords. We leverage machine learning to model temporal patterns around bursts in Twitter's unfiltered public sample stream and build a classifier to identify tokens experiencing these bursts. We show LABurst performs competitively with existing burst detection techniques while simultaneously providing insight into and detection of unanticipated moments. To demonstrate our approach's potential, we compare two baseline event-detection algorithms with our language-agnostic algorithm to detect key moments across three major sporting competitions: 2013 World Series, 2014 Super Bowl, and 2014 World Cup. Our results show LABurst outperforms a time series analysis baseline and is competitive with a domain-specific baseline even though we operate without any domain knowledge. We then go further by transferring LABurst's models learned in the sports domain to the task of identifying earthquakes in Japan and show our method detects large spikes in earthquake-related tokens within two minutes of the actual event. | Learning to Discover Key Moments in Social Media Streams | 9,058 |
Privacy has become a major concern in Online Social Networks (OSNs) due to threats such as advertising spam, online stalking and identity theft. Although many users hide or do not fill out their private attributes in OSNs, prior studies point out that the hidden attributes may be inferred from some other public information. Thus, users' private information could still be at stake to be exposed. Hitherto, little work helps users to assess the exposure probability/risk that the hidden attributes can be correctly predicted, let alone provides them with pointed countermeasures. In this article, we focus our study on the exposure risk assessment by a particular privacy-sensitive attribute - current city - in Facebook. Specifically, we first design a novel current city prediction approach that discloses users' hidden `current city' from their self-exposed information. Based on 371,913 Facebook users' data, we verify that our proposed prediction approach can predict users' current city more accurately than state-of-the-art approaches. Furthermore, we inspect the prediction results and model the current city exposure probability via some measurable characteristics of the self-exposed information. Finally, we construct an exposure estimator to assess the current city exposure risk for individual users, given their self-exposed information. Several case studies are presented to illustrate how to use our proposed estimator for privacy protection. | Are You Really Hidden? Predicting Current City from Profile and Social
Relationship | 9,059 |
Background: Twitter has become the "wild-west" of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, "kid-friendly" flavors, algorithmically generated false testimonials, and free samples. Methods:All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching. Results:The overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words ban,tobacco,doesn't,drug,against,poison,tax and a relative decrease in the positive words like haha,good,cool. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words best,win,buy,sale,health,discount and a relative decrease in negative words like bad, hate, stupid, don't. Conclusions:Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing. | Vaporous Marketing: Uncovering Pervasive Electronic Cigarette
Advertisements on Twitter | 9,060 |
Recently, graph matching algorithms have been successfully applied to the problem of network de-anonymization, in which nodes (users) participating to more than one social network are identified only by means of the structure of their links to other members. This procedure exploits an initial set of seed nodes large enough to trigger a percolation process which correctly matches almost all other nodes across the different social networks. Our main contribution is to show the crucial role played by clustering, which is a ubiquitous feature of realistic social network graphs (and many other systems). Clustering has both the effect of making matching algorithms more vulnerable to errors, and the potential to dramatically reduce the number of seeds needed to trigger percolation, thanks to a wave-like propagation effect. We demonstrate these facts by considering a fairly general class of random geometric graphs with variable clustering level, and showing how clever algorithms can achieve surprisingly good performance while containing matching errors. | Impact of Clustering on the Performance of Network De-anonymization | 9,061 |
In the widely used message platform Twitter, about 2% of the tweets contains the geographical location through exact GPS coordinates (latitude and longitude). Knowing the location of a tweet is useful for many data analytics questions. This research is looking at the determination of a location for tweets that do not contain GPS coordinates. An accuracy of 82% was achieved using a Naive Bayes model trained on features such as the users' timezone, the user's language, and the parsed user location. The classifier performs well on active Twitter countries such as the Netherlands and United Kingdom. An analysis of errors made by the classifier shows that mistakes were made due to limited information and shared properties between countries such as shared timezone. A feature analysis was performed in order to see the effect of different features. The features timezone and parsed user location were the most informative features. | Determine the User Country of a Tweet | 9,062 |
Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag. | Is That Twitter Hashtag Worth Reading | 9,063 |
We investigate certain structural properties of random interdependent networks. We start by studying a property known as $r$-robustness, which is a strong indicator of the ability of a network to tolerate structural perturbations and dynamical attacks. We show that random $k$-partite graphs exhibit a threshold for $r$-robustness, and that this threshold is the same as the one for the graph to have minimum degree $r$. We then extend this characterization to random interdependent networks with arbitrary intra-layer topologies. Finally, we characterize the algebraic connectivity of such networks, and provide an asymptotically tight rate of growth of this quantity for a certain range of inter-layer edge formation probabilities. Our results arise from a characterization of the isoperimetric constant of random interdependent networks, and yield new insights into the structure and robustness properties of such networks. | Robustness and Algebraic Connectivity of Random Interdependent Networks | 9,064 |
This work is motivated by the dearth of research that deals with social media content created from the Basque Country or written in Basque language. While social fingerprints during events have been analysed in numerous other locations and languages, this article aims to fill this gap so as to initiate a much-needed research area within the Basque scientific community. To this end, we describe the methodology we followed to collect tweets posted during the quintessential exhibition race in support of the Basque language. We also present the results of the analysis of these tweets. Our analysis shows that the most eventful moments lead to spikes in tweeting activity, producing more tweets. Furthermore, we emphasize the importance of having an official account for the event in question, which helps improve the visibility of the event in the social network as well as the dissemination of information to the Basque community. Along with the official account, journalists and news organisations play a crucial role in the diffusion of information. | Euskahaldun: Euskararen Aldeko Martxa Baten Sare Sozialetako Islaren
Bilketa eta Analisia | 9,065 |
Online social networking sites such as Facebook, Twitter and Flickr are among the most popular sites on the Web, providing platforms for sharing information and interacting with a large number of people. The different ways for users to interact, such as liking, retweeting and favoriting user-generated content, are among the defining and extremely popular features of these sites. While empirical studies have been done to learn about the network growth processes in these sites, few studies have focused on social interaction behaviour and the effect of social interaction on network growth. In this paper, we analyze large-scale data collected from the Flickr social network to learn about individual favoriting behaviour and examine the occurrence of link formation after a favorite is created. We do this using a systematic formulation of Flickr as a two-layer temporal multiplex network: the first layer describes the follow relationship between users and the second layer describes the social interaction between users in the form of favorite markings to photos uploaded by them. Our investigation reveals that (a) favoriting is well-described by preferential attachment, (b) over 50% of favorites are reciprocated within 10 days if at all they are reciprocated, (c) different kinds of favorites differ in how fast they are reciprocated, and (d) after a favorite is created, multiplex triangles are closed by the creation of follow links by the favoriter's followers to the favorite receiver. | Social Interaction in the Flickr Social Network | 9,066 |
Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context. | Measuring user influence on Twitter: A survey | 9,067 |
Seeded PageRank is an important network analysis tool for identifying and studying regions nearby a given set of nodes, which are called seeds. The seeded PageRank vector is the stationary distribution of a random walk that randomly resets at the seed nodes. Intuitively, this vector is concentrated nearby the given seeds, but is mathematically non-zero for all nodes in a connected graph. We study this concentration, or localization, and show a sublinear upper bound on the number of entries required to approximate seeded PageRank on all graphs with a natural type of skewed-degree sequence---similar to those that arise in many real-world networks. Experiments with both real-world and synthetic graphs give further evidence to the idea that the degree sequence of a graph has a major influence on the localization behavior of seeded PageRank. Moreover, we establish that this localization is non-trivial by showing that complete-bipartite graphs produce seeded PageRank vectors that cannot be approximated with a sublinear number of non-zeros. | Localization in Seeded PageRank | 9,068 |
The availability of big data on human activity is currently changing the way we look at our surroundings. With the high penetration of mobile phones, nearly everyone is already carrying a high-precision sensor providing an opportunity to monitor and analyze the dynamics of human movement on unprecedented scales. In this article, we present a technique and visualization tool which uses aggregated activity measures of mobile networks to gain information about human activity shaping the structure of the cities. Based on ten months of mobile network data, activity patterns can be compared through time and space to unravel the "city's pulse" as seen through the specific signatures of different locations. Furthermore, the tool allows classifying the neighborhoods into functional clusters based on the timeline of human activity, providing valuable insights on the actual land use patterns within the city. This way, the approach and the tool provide new ways of looking at the city structure from historical perspective and potentially also in real-time based on dynamic up-to-date records of human behavior. The online tool presents results for four global cities: New York, London, Hong Kong and Los Angeles. | Visualizing signatures of human activity in cities across the globe | 9,069 |
The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data. | Using a Power Law Distribution to describe Big Data | 9,070 |
Rumor source identification in large social networks has received significant attention lately. Most recent works deal with the scale of the problem by observing a subset of the nodes in the network, called sensors, to estimate the source. This paper addresses the problem of locating the source of a rumor in large social networks where some of these sensor nodes have failed. We estimate the missing information about the sensors using doubly non-negative (DN) matrix completion and compressed sensing techniques. This is then used to identify the actual source by using a maximum likelihood estimator we developed earlier, on a large data set from Sina Weibo. Results indicate that the estimation techniques result in almost as good a performance of the ML estimator as for the network for which complete information is available. To the best of our knowledge, this is the first research work on source identification with incomplete information in social networks. | Identification of Source of Rumors in Social Networks with Incomplete
Information | 9,071 |
Internet and online-based social systems are rising as the dominant mode of communication in society. However, the public or semi-private environment under which most online communications operate under do not make them suitable channels for speaking with others about personal or emotional problems. This has led to the emergence of online platforms for emotional support offering free, anonymous, and confidential conversations with live listeners. Yet very little is known about the way these platforms are utilized, and if their features and design foster strong user engagement. This paper explores the utilization and the interaction features of hundreds of thousands of users on 7 Cups of Tea, a leading online platform offering online emotional support. It dissects the level of activity of hundreds of thousands of users, the patterns by which they engage in conversation with each other, and uses machine learning methods to find factors promoting engagement. The study may be the first to measure activities and interactions in a large-scale online social system that fosters peer-to-peer emotional support. | Stay Awhile and Listen: User Interactions in a Crowdsourced Platform
Offering Emotional Support | 9,072 |
Social media has been contributing many research areas such as data mining, recommender systems, time series analysis, etc. However, there are not many successful applications regarding social media in government agencies. In fact, lots of governments have social media accounts such as twitter and facebook. More and more customers are likely to communicate with governments on social media, causing massive external social media data for governments. This external data would be beneficial for analysing behaviours and real needs of the customers. Besides this, most governments also have a call centre to help customers solve their problems. It is not difficult to imagine that the enquiries on external social media and internal call centre may have some coupling relationships. The couplings could be helpful for studying customers' intent and allocating government's limited resources for better service. In this paper, we mainly focus on analysing the coupling relations between internal call centre and external public media using time series analysis methods for Australia Department of Immigration and Border Protec-tion. The discovered couplings demonstrate that call centre and public media indeed have correlations, which are significant for understanding customers' behaviours. | Coupling Analysis Between Twitter and Call Centre | 9,073 |
User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, they do not necessarily succeed at pulling like-minded users that share the same interests together. In this paper, we are interested in identifying communities of users that share similar topical interests over time, regardless of whether they are explicitly connected to each other on the social network. More specifically, we tackle the problem of identifying temporal topic-based communities from Twitter, i.e., communities of users who have similar temporal inclination towards the current emerging topics on Twitter. We model each topic as a collection of highly correlated semantic concepts observed in tweets and identify them by clustering the time-series based representation of each concept built based on each concept's observation frequency over time. Based on the identified emerging topics in a given time period, we utilize multivariate time series analysis to model the contributions of each user towards the identified topics, which allows us to detect latent user communities. Through our experiments on Twitter data, we demonstrate i) the effectiveness of our topic detection method to detect real world topics and ii) the effectiveness of our approach compared to well-established approaches for community detection. | Temporal Identification of Latent Communities on Twitter | 9,074 |
Many real networks such as the World Wide Web, financial, biological, citation and social networks have a power-law degree distribution. Networks with this feature are also called scale-free. Several models for producing scale-free networks have been obtained by now and most of them are based on the preferential attachment approach. We will offer the model with another scale-free property explanation. The main idea is to approximate the network's adjacency matrix by multiplication of the matrices $V$ and $V^T$, where $V$ is the matrix of vertices' latent features. This approach is called matrix factorization and is successfully used in the link prediction problem. To create a generative model of scale-free networks we will sample latent features $V$ from some probabilistic distribution and try to generate a network's adjacency matrix. Entries in the generated matrix are dot products of latent features which are real numbers. In order to create an adjacency matrix, we approximate entries with the Boolean domain $\{0, 1\}$. We have incorporated the threshold parameter $\theta$ into the model for discretization of a dot product. Actually, we have been influenced by the geographical threshold models which were recently proven to have good results in a scale-free networks generation. The overview of our results is the following. First, we will describe our model formally. Second, we will tune the threshold $\theta$ in order to generate sparse growing networks. Finally, we will show that our model produces scale-free networks with the fixed power-law exponent which equals two. In order to generate oriented networks with tunable power-law exponents and to obtain other model properties, we will offer different modifications of our model. Some of our results will be demonstrated using computer simulation. | Factorization threshold models for scale-free networks generation | 9,075 |
Since network motifs are an important property of networks and some networks have the behaviors of rewiring or reducing or adding edges between old vertices before new vertices entering the networks, we construct our non-randomized model N(t) and randomized model N'(t) that have the predicated fixed subgraphs like motifs and satisfy both properties of growth and preferential attachment by means of the recursive algorithm from the lower levels of the so-called bound growing network models. To show the scale-free property of the randomized model N'(t), we design a new method, called edge-cumulative distribution, and democrat two edge-cumulative distributions of N(t) and N'(t) are equivalent to each other. | Growing Network Models Having Part Edges Removed/added Randomly | 9,076 |
Twitter is one of the most popular social media. Due to the ease of availability of data, Twitter is used significantly for research purposes. Twitter is known to evolve in many aspects from what it was at its birth; nevertheless, how it evolved its own linguistic style is still relatively unknown. In this paper, we study the evolution of various sociolinguistic aspects of Twitter over large time scales. To the best of our knowledge, this is the first comprehensive study on the evolution of such aspects of this OSN. We performed quantitative analysis both on the word level as well as on the hashtags since it is perhaps one of the most important linguistic units of this social media. We studied the (in)formality aspects of the linguistic styles in Twitter and find that it is neither fully formal nor completely informal; while on one hand, we observe that Out-Of-Vocabulary words are decreasing over time (pointing to a formal style), on the other hand it is quite evident that whitespace usage is getting reduced with a huge prevalence of running texts (pointing to an informal style). We also analyze and propose quantitative reasons for repetition and coalescing of hashtags in Twitter. We believe that such phenomena may be strongly tied to different evolutionary aspects of human languages. | Out of vocabulary words decrease, running texts prevail and hashtags
coalesce: Twitter as an evolving sociolinguistic system | 9,077 |
A social network grows over a period of time with the formation of new connections and relations. In recent years we have witnessed a massive growth of online social networks like Facebook, Twitter etc. So it has become a problem of extreme importance to know the destiny of these networks. Thus predicting the evolution of a social network is a question of extreme importance. A good model for evolution of a social network can help in understanding the properties responsible for the changes occurring in a network structure. In this paper we propose such a model for evolution of social networks. We model the social network as an undirected graph where nodes represent people and edges represent the friendship between them. We define the evolution process as a set of rules which resembles very closely to how a social network grows in real life. We simulate the evolution process and show, how starting from an initial network, a network evolves using this model. We also discuss how our model can be used to model various complex social networks other than online social networks like political networks, various organizations etc.. | Evolving Social Networks via Friend Recommendations | 9,078 |
How to tell if a review is real or fake? What does the underworld of fraudulent reviewing look like? Detecting suspicious reviews has become a major issue for many online services. We propose the use of a clique-finding approach to discover well-organized suspicious reviewers. From a Yelp dataset with over one million reviews, we construct multiple Reviewer Similarity graphs to link users that have unusually similar behavior: two reviewers are connected in the graph if they have reviewed the same set of venues within a few days. From these graphs, our algorithms extracted many large cliques and quasi-cliques, the largest one containing a striking 11 users who coordinated their review activities in identical ways. Among the detected cliques, a large portion contain Yelp Scouts who are paid by Yelp to review venues in new areas. Our work sheds light on their little-known operation. | Spotting Suspicious Reviews via (Quasi-)clique Extraction | 9,079 |
Human trafficking is a challenging law enforcement problem, and a large amount of such activity manifests itself on various online forums. Given the large, heterogeneous and noisy structure of this data, building models to predict instances of trafficking is an even more convolved a task. In this paper we propose and entity resolution pipeline using a notion of proxy labels, in order to extract clusters from this data with prior history of human trafficking activity. We apply this pipeline to 5M records from backpage.com and report on the performance of this approach, challenges in terms of scalability, and some significant domain specific characteristics of our resolved entities. | An Entity Resolution approach to isolate instances of Human Trafficking
online | 9,080 |
The U.S.-China relationship is arguably the most important bilateral relationship in the 21st century. Typically it is measured through opinion polls, for example, by Gallup and Pew Institute. In this paper, we propose a new method to measure U.S.-China relations using data from Twitter, one of the most popular social networks. Compared with traditional opinion polls, our method has two distinctive advantages. First, our sample size is significantly larger. National opinion polls have at most a few thousand samples. Our data set has 724,146 samples. The large size of our data set enables us to perform state level analysis, which so far even large opinion polls have left unexplored. Second, our method can control for fixed state and date effects. We first demonstrate the existence of inter-state and inter-day variances and then control for these variances in our regression analysis. Empirically, our study is able to replicate the stylized results from opinion polls as well as generate new insights. At the state level, we find New York, Michigan, Indiana and Arizona are the top four most China-friendly states. Wyoming, South Dakota, Kansas and Nevada are most homogeneous. At the individual level, we find attitudes towards China improve as an individual's Twitter experience grows longer and more intense. We also find individuals of Chinese ethnicity are statistically more China-friendly. | America Tweets China: A Fine-Grained Analysis of the State and
Individual Characteristics Regarding Attitudes towards China | 9,081 |
Counting the frequencies of 3-, 4-, and 5-node undirected motifs (also know as graphlets) is widely used for understanding complex networks such as social and biology networks. However, it is a great challenge to compute these metrics for a large graph due to the intensive computation. Despite recent efforts to count triangles (i.e., 3-node undirected motif counting), little attention has been given to developing scalable tools that can be used to characterize 4- and 5-node motifs. In this paper, we develop computational efficient methods to sample and count 4- and 5- node undirected motifs. Our methods provide unbiased estimators of motif frequencies, and we derive simple and exact formulas for the variances of the estimators. Moreover, our methods are designed to fit vertex centric programming models, so they can be easily applied to current graph computing systems such as Pregel and GraphLab. We conduct experiments on a variety of real-word datasets, and experimental results show that our methods are several orders of magnitude faster than the state-of-the-art methods under the same estimation errors. | Moss: A Scalable Tool for Efficiently Sampling and Counting 4- and
5-Node Graphlets | 9,082 |
Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases. | #Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag
Compounds | 9,083 |
In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives. | Correlated Cascades: Compete or Cooperate | 9,084 |
Our goal is to determine the structural differences between different categories of networks and to use these differences to predict the network category. Existing work on this topic has looked at social networks such as Facebook, Twitter, co-author networks etc. We, instead, focus on a novel data set that we have assembled from a variety of sources, including law-enforcement agencies, financial institutions, commercial database providers and other similar organizations. The data set comprises networks of "persons of interest" with each network belonging to different categories such as suspected terrorists, convicted individuals etc. We demonstrate that such "anti-social" networks are qualitatively different from the usual social networks and that new techniques are required to identify and learn features of such networks for the purposes of prediction and classification. We propose Cliqster, a new generative Bernoulli process-based model for unweighted networks. The generating probabilities are the result of a decomposition which reflects a network's community structure. Using a maximum likelihood solution for the network inference leads to a least-squares problem. By solving this problem, we are able to present an efficient algorithm for transforming the network to a new space which is both concise and discriminative. This new space preserves the identity of the network as much as possible. Our algorithm is interpretable and intuitive. Finally, by comparing our research against the baseline method (SVD) and against a state-of-the-art Graphlet algorithm, we show the strength of our algorithm in discriminating between different categories of networks. | On The Network You Keep: Analyzing Persons of Interest using Cliqster | 9,085 |
Growing role of intellectual capital within organizations is affecting new strategies related to knowledge management and competence development. Among different aspects related to this field, knowledge diffusion has become one of interesting areas from both practitioner and researchers perspective. Several models were proposed with main goal to simulate diffusion and to explain the nature of these processes. Existing models are focused on knowledge diffusion and they assume diffusion within a single layer using knowledge representation. From the organizational perspective connecting several types of knowledge and modelling changes of competence can bring additional value. In the article we extended existing approaches by using multilayer diffusion model and focused on analysis of competence development process. The proposed model describes competence development process in a new way through horizontal and vertical knowledge diffusion in multilayer network. In the network, agents collaborate and interchange various kind of knowledge through different layers and this mutual activities affect the competences in a positive or negative way. Taking under consideration workers cognitive and social abilities and the previous level of competence the new competence level can be estimated. The model is developed to support competence management in different organizations. | Model of Multilayer Knowledge Diffusion for Competence Development in an
Organization | 9,086 |
Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements. | Endorsement Deduction and Ranking in Social Networks | 9,087 |
The task of \emph{community detection} in a graph formalizes the intuitive task of grouping together subsets of vertices such that vertices within clusters are connected tighter than those in disparate clusters. This paper approaches community detection in graphs by constructing Markov random walks on the graphs. The mixing properties of the random walk are then used to identify communities. We use coupling from the past as an algorithmic primitive to translate the mixing properties of the walk into revealing the community structure of the graph. We analyze the performance of our algorithms on specific graph structures, including the stochastic block models (SBM) and LFR random graphs. | Community Detection Using Slow Mixing Markov Models | 9,088 |
The short-time Fourier transform (STFT) is widely used to analyze the spectra of temporal signals that vary through time. Signals defined over graphs, due to their intrinsic complexity, exhibit large variations in their patterns. In this work we propose a new formulation for an STFT for signals defined over graphs. This formulation draws on recent ideas from spectral graph theory, using personalized PageRank vectors as its fundamental building block. Furthermore, this work establishes and explores the connection between local spectral graph theory and localized spectral analysis of graph signals. We accompany the presentation with synthetic and real-world examples, showing the suitability of the proposed approach. | A short-graph Fourier transform via personalized PageRank vectors | 9,089 |
Ever since first mobile phones equipped with GPS came to the market, knowing the exact user location has become a holy grail of almost every service that lives in the digital world. Starting with the idea of location based services, nowadays it is not only important to know where users are in real time, but also to be able predict where they will be in future. Moreover, it is not enough to know user location in form of latitude longitude coordinates provided by GPS devices, but also to give a place its meaning (i.e., semantically label it), in particular detecting the most probable home location for the given user. The aim of this paper is to provide novel insights on differences among the ways how different types of human digital trails represent the actual mobility patterns and therefore the differences between the approaches interpreting those trails for inferring said patterns. Namely, with the emergence of different digital sources that provide information about user mobility, it is of vital importance to fully understand that not all of them capture exactly the same picture. With that being said, in this paper we start from an example showing how human mobility patterns described by means of radius of gyration are different for Flickr social network and dataset of bank card transactions. Rather than capturing human movements closer to their homes, Flickr more often reveals people travel mode. Consequently, home location inferring methods used in both cases cannot be the same. We consider several methods for home location definition known from the literature and demonstrate that although for bank card transactions they provide highly consistent results, home location definition detection methods applied to Flickr dataset happen to be way more sensitive to the method selected, stressing the paramount importance of adjusting the method to the specific dataset being used. | Choosing the right home location definition method for the given dataset | 9,090 |
In many applications, we need to measure similarity between nodes in a large network based on features of their neighborhoods. Although in-network node similarity based on proximity has been well investigated, surprisingly, measuring in-network node similarity based on neighborhoods remains a largely untouched problem in literature. One grand challenge is that in different applications we may need different measurements that manifest different meanings of similarity. In this paper, we investigate the problem in a principled and systematic manner. We develop a unified parametric model and a series of four instance measures. Those instance similarity measures not only address a spectrum of various meanings of similarity, but also present a series of tradeoffs between computational cost and strictness of matching between neighborhoods of nodes being compared. By extensive experiments and case studies, we demonstrate the effectiveness of the proposed model and its instances. | In-Network Neighborhood-Based Node Similarity Measure: A Unified
Parametric Model | 9,091 |
As the world becomes more digitized and interconnected, information that was once considered to be private such as one's health status is now being shared publicly. To understand this new phenomenon better, it is crucial to study what types of health information are being shared on social media and why, as well as by whom. In this paper, we study the traits of users who share their personal health and fitness related information on social media by analyzing fitness status updates that MyFitnessPal users have shared via Twitter. We investigate how certain features like user profile, fitness activity, and fitness network in social media can potentially impact the long-term engagement of fitness app users. We also discuss implications of our findings to achieve a better retention of these users and to promote more sharing of their status updates. | Persistent Sharing of Fitness App Status on Twitter | 9,092 |
We propose a new user-centric imagery of the WWW that foregrounds local usage and its shaping forces, in contrast to existing imageries that prioritize Internet infrastructure. We construct ethnological maps of WWW usage through a network analysis of shared global traffic between 1000 most popular websites at three time points and develop granular measures for exploring global participation in online communication. Our results reveal the significant growth and thickening of online regional cultures associated with the global South. We draw attention to how local cultural identity, affirmative state intervention and economic contexts shape regional cultures on the global WWW. | Reimagining Internet Geographies: A User-Centric Ethnological Mapping of
the World Wide Web | 9,093 |
The matrix factorization (MF) technique has been widely adopted for solving the rating prediction problem in recommender systems. The MF technique utilizes the latent factor model to obtain static user preferences (user latent vectors) and item characteristics (item latent vectors) based on historical rating data. However, in the real world user preferences are not static but full of dynamics. Though there are several previous works that addressed this time varying issue of user preferences, it seems (to the best of our knowledge) that none of them is specifically designed for tracking concept drift in individual user preferences. Motivated by this, we develop a Temporal Matrix Factorization approach (TMF) for tracking concept drift in each individual user latent vector. There are two key innovative steps in our approach: (i) we develop a modified stochastic gradient descent method to learn an individual user latent vector at each time step, and (ii) by the Lasso regression we learn a linear model for the transition of the individual user latent vectors. We test our method on a synthetic dataset and several real datasets. In comparison with the original MF, our experimental results show that our temporal method is able to achieve lower root mean square errors (RMSE) for both the synthetic and real datasets. One interesting finding is that the performance gain in RMSE is mostly from those users who indeed have concept drift in their user latent vectors at the time of prediction. In particular, for the synthetic dataset and the Ciao dataset, there are quite a few users with that property and the performance gains for these two datasets are roughly 20% and 5%, respectively. | Temporal Matrix Factorization for Tracking Concept Drift in Individual
User Preferences | 9,094 |
Facebook is the world's largest Online Social Network, having more than 1 billion users. Like most other social networks, Facebook is home to various categories of hostile entities who abuse the platform by posting malicious content. In this paper, we identify and characterize Facebook pages that engage in spreading URLs pointing to malicious domains. We used the Web of Trust API to determine domain reputations of URLs published by pages, and identified 627 pages publishing untrustworthy information, misleading content, adult and child unsafe content, scams, etc. which are deemed as "Page Spam" by Facebook, and do not comply with Facebook's community standards. Our findings revealed dominant presence of politically polarized entities engaging in spreading content from untrustworthy web domains. Anger and religion were the most prominent topics in the textual content published by these pages. We found that at least 8% of all malicious pages were dedicated to promote a single malicious domain. Studying the temporal posting activity of pages revealed that malicious pages were more active than benign pages. We further identified collusive behavior within a set of malicious pages spreading adult and pornographic content. We believe our findings will enable technologists to devise efficient automated solutions to identify and curb the spread of malicious content through such pages. To the best of our knowledge, this is the first attempt in literature, focused exclusively on characterizing malicious Facebook pages. | Hiding in Plain Sight: The Anatomy of Malicious Facebook Pages | 9,095 |
We consider the propagation of a contagion process (epidemic) on a network and study the problem of dynamically allocating a fixed curing budget to the nodes of the graph, at each time instant. For bounded degree graphs, we provide a lower bound on the expected time to extinction under any such dynamic allocation policy, in terms of a combinatorial quantity that we call the resistance of the set of initially infected nodes, the available budget, and the number of nodes n. Specifically, we consider the case of bounded degree graphs, with the resistance growing linearly in n. We show that if the curing budget is less than a certain multiple of the resistance, then the expected time to extinction grows exponentially with n. As a corollary, if all nodes are initially infected and the CutWidth of the graph grows linearly, while the curing budget is less than a certain multiple of the CutWidth, then the expected time to extinction grows exponentially in n. The combination of the latter with our prior work establishes a fairly sharp phase transition on the expected time to extinction (sub-linear versus exponential) based on the relation between the CutWidth and the curing budget. | When is a network epidemic hard to eliminate? | 9,096 |
We consider an SIS-type epidemic process that evolves on a known graph. We assume that a fixed curing budget can be allocated at each instant to the nodes of the graph, towards the objective of minimizing the expected extinction time of the epidemic. We provide a lower bound on the optimal expected extinction time as a function of the available budget, the epidemic parameters, the maximum degree, and the CutWidth of the graph. For graphs with large CutWidth (close to the largest possible), and under a budget which is sublinear in the number of nodes, our lower bound scales exponentially with the size of the graph. | A lower bound on the performance of dynamic curing policies for
epidemics on graphs | 9,097 |
Influence maximization(IM) problem is to find a seed set in a social network which achieves the maximal influence spread. This problem plays an important role in viral marketing. Numerous models have been proposed to solve this problem. However, none of them considers the attributes of nodes. Paying all attention to the structure of network causes some trouble applying these models to real-word applications. Motivated by this, we present weighted independent cascade (WIC) model, a novel cascade model which extends the applicability of independent cascade(IC) model by attaching attributes to the nodes. The IM problem in WIC model is to maximize the value of nodes which are influenced. This problem is NP-hard. To solve this problem, we present a basic greedy algorithm and Weight Reset(WR) algorithm. Moreover, we propose Bounded Weight Reset(BWR) algorithm to make further effort to improve the efficiency by bounding the diffusion node influence. We prove that BWR is a fully polynomial-time approximation scheme(FPTAS). Experimentally, we show that with additional node attribute, the solution achieved by WIC model outperforms that of IC model in nearly 90%. The experimental results show that BWR can achieve excellent approximation and faster than greedy algorithm more than three orders of magnitude with little sacrifice of accuracy. Especially, BWR can handle large networks with millions of nodes in several tens of seconds while keeping rather high accuracy. Such result demonstrates that BWR can solve IM problem effectively and efficiently. | Efficient Influence Maximization in Weighted Independent Cascade Model | 9,098 |
In this paper we show how to efficiently produce unbiased estimates of subgraph frequencies from a probability sample of egocentric networks (i.e., focal nodes, their neighbors, and the induced subgraphs of ties among their neighbors). A key feature of our proposed method that differentiates it from prior methods is the use of egocentric data. Because of this, our method is suitable for estimation in large unknown graphs, is easily parallelizable, handles privacy sensitive network data (e.g. egonets with no neighbor labels), and supports counting of large subgraphs (e.g. maximal clique of size 205 in Section 6) by building on top of existing exact subgraph counting algorithms that may not support sampling. It gracefully handles a variety of sampling designs such as uniform or weighted independence or random walk sampling. Our method can be used for subgraphs that are: (i) undirected or directed; (ii) induced or non-induced; (iii) maximal or non-maximal; and (iv) potentially annotated with attributes. We compare our estimators on a variety of real-world graphs and sampling methods and provide suggestions for their use. Simulation shows that our method outperforms the state-of-the-art approach for relative subgraph frequencies by up to an order of magnitude for the same sample size. Finally, we apply our methodology to a rare sample of Facebook users across the social graph to estimate and interpret the clique size distribution and gender composition of cliques. | Estimating Subgraph Frequencies with or without Attributes from
Egocentrically Sampled Data | 9,099 |
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