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## RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
### Haoyu Han[1], Yu Wang[2], Harry Shomer[1], Yongjia Lei, [2], Kai Guo [1], Zhigang Hua[3], Bo Long [3], Hui Liu[1], Jiliang Tang[1]
1Michigan State University, 2University of Oregon, 3Meta
### {hanhaoy1, shomerha, guokai1, liuhui7, tangjili}@msu.edu {yu... | {
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tracts a Knowledge Graph (KG) from text and performs retrieval solely based on the KG and (2)
Community-based GraphRAG (Edge et al., 2024),
which retrieves information not only from the constructed KG but also from hierarchical communities within the graph. For the Question Answering task, we conduct experiments on bot... | {
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Figure 1: The illustration of RAG, KG-based GraphRAGs and Community-based GraphRAGs.
Despite leveraging the existing graphs, recent
studies have explored incorporating graph construction into GraphRAG to enhance text-based
tasks. For example, Dong et al. (2024) construct
document graphs using Abstract Meaning Represe... | {
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content. Additionally, for each triplet, we can
retrieve the corresponding text associated with
it. We define two variants of KG-GraphRAG: (1)
_KG-GraphRAG (Triplets), which retrieves only the_
triplets, and (2) KG-GraphRAG (Triplets+Text),
which retrieves both the triplets and their associated
source text. We implemen... | {
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Table 1: Performance comparison (%) on NQ and Hotpot datasets. The best results are highlighted in bold, and the
second-best results are underlined.
**NQ** **Hotpot**
**Method** **Llama 3.1-8B** **Llama 3.1-70B** **Llama 3.1-8B** **Llama 3.1-70B**
P R F1 P R F1 P R F1 P R F1
RAG **71.7** **63.93** **64.78** **74.55... | {
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40.0 17.1
13.7 29.1
45.4 9.2
9.8 35.6
(a) NQ
47.2 7.8
6.0 39.0
(b) Hotpot
55.4 11.6
13.6 19.4
(c) MultiHop-RAG
(d)... | {
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(a) Llama3.1-8B (b) Llama3.1-70B
Figure 3: Overall QA performance comparison of different methods.
We evaluate both the KG-based and Communitybased GraphRAG methods, along with the Integration strategy discussed in Section 4.4. The results of Llama3.1-8B model on Query-based single
document summarization and multipl... | {
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Table 4: The performance of query-based single document summarization task using Llama3.1-8B.
**SQuALITY** **QMSum**
**Method** **ROUGE-2** **BERTScore** **ROUGE-2** **BERTScore**
P R F1 P R F1 P R F1 P R F1
RAG 15.09 8.74 10.08 74.54 81.00 77.62 21.50 **3.80** 6.32 **81.03** 84.45 **82.69**
KG-GraphRAG (Triplets o... | {
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### Limitations
In this paper, we evaluate and compare RAG and
GraphRAG on Question Answering and Querybased Summarization tasks. Future work can extend this study to additional tasks to further assess
the strengths and applicability of GraphRAG. Additionally, the graph construction in all GraphRAG
methods explored in... | {
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retrieval on structured data. _arXiv preprint_
_arXiv:2305.19912._
Yongqi Li, Wenjie Li, and Liqiang Nie. 2022. Dynamic
graph reasoning for conversational open-domain
question answering. ACM Transactions on Infor_mation Systems (TOIS), 40(4):1–24._
Chin-Yew Lin. 2004. Rouge: A package for automatic
evaluation of summ... | {
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Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling.
2024. Corrective retrieval augmented generation.
_arXiv preprint arXiv:2401.15884._
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and
Christopher D Manning. 2018. Hotpotqa: A dataset
for diverse, explainable multi-hop qu... | {
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### A Appendix
**A.1** **Dataset**
In this section, we introduce the used datasets in the question answering tasks and query-based summarization tasks.
**A.1.1** **Question Answering**
In the QA tasks, we use the following four widely used datasets:
- Natural Questions (NQ) (Kwiatkowski et al., 2019): The NQ dat... | {
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- ODSum (Zhou et al., 2023): The ODSum dataset is designed to evaluate modern summarization
models in multi-document contexts and consists of two subsets: ODSum-story and ODSum-meeting.
ODSum-story is derived from the SQuALITY dataset, while ODSum-meeting is constructed from
QMSum. We use both ODSum-story and ODSum-m... | {
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Table 10: The performance of Community-GraphRAG (Local) with Llama 3.1-70B model on NovelQA dataset.
Community-GraphRAG (Local) character meaning plot relat settg span times avg
mh 77.08 70.59 63.89 77.53 92.31 28 32.35 46.68
sh 68.42 71.43 74.9 62.5 74.23 - - 72.44
dtl 55.71 37.08 69.64 64.29 75.68 ... | {
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Table 13: The performance of query-based multiple document summarization task using Llama3.1-70B.
ODSum-story ODSum-meeting
Method ROUGE-2 BERTScore ROUGE-2 BERTScore
P R F1 P R F1 P R F1 P R
RAG 15.60 9.98 11.09 74.80 81.29 77.89 18.81 6.41 8.97 83.56 85.16
KG-GraphRAG(Triplets only) 10.08 9.12 8.48 75.71 81.93 78... | {
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|RAG-Order 1 GraphRAG-Local-Order 1 RAG-Order 2 GraphRAG-Local-Order 2|Col2|Col3|Col4|RAG-Order 1 GraphRAG-Local-Order 1 RAG-Order 2 GraphRAG-Local-Order 2|Col6|Col7|Col8|Col9|Col10|
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... | {
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## Retrieval-Augmented Generation with Graphs (GraphRAG)
**Haoyu Han[1][∗], Yu Wang[2][∗], Harry Shomer[1], Kai Guo[1], Jiayuan Ding[5], Yongjia Lei[2],**
**Mahantesh Halappanavar[3], Ryan A. Rossi[4], Subhabrata Mukherjee[5], Xianfeng Tang[6], Qi He[6],**
**Zhigang Hua[7], Bo Long[7], Tong Zhao[8], Neil Shah[8], Ami... | {
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consultation [467, 472, 515]). Moreover, recent advancements in large language models (LLMs)
have further underscored the power of RAG in enhancing the social responsibility of LLMs, such as
mitigating hallucinations [397], enhancing interpretability and transparency [203], enabling dynamic
adaptability [360, 419], red... | {
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star Methane, and 3-star T-junction vs. 4-square road, is essential in drug design for disease
treatment [139] and road construction for city planning [209]. Beyond the retriever, the generator
also requires specialized designs. When retrieved content includes complex graph structures
with textual attributes, simply ve... | {
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Figure 1: RAG works on text and images, which can be uniformly formatted as 1D sequences or 2D
grids with no relational information. In contrast, GraphRAG works on graph-structured data, which
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Figure 3: A holistic framework of GraphRAG and representative techniques for its key components.
### 2 A Holistic Framework of GraphRAG
Based on existing literature on GraphRAG, we present a holistic framework of GraphRAG. Next, we
introduce the basic problem setting and... | {
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Table 1: Summary of Task Applications and Exemplary Queries for GraphRAG in each domain.
-----
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Figure 4: Existing techniques of query processor Ω[Processor] in GraphRAG.
Table 2: Difference of query processor Ω[Processor] between RAG and GraphRAG.
**Technique** **RAG** **GraphRAG**
Entity Recognition Extracting mentions in knowledge bases Extracting mentioned node... | {
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match the recognized types for next-round exploration. For example, given the question "Who are the
authors of ‘Language Models are Unsupervised Multi-task Learners’?" the initially recognized entity
should not only be based on the semantic name "Language Models are Unsupervised Multi-task
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"p... |
Figure 5: Visualizing representative retrievers used in GraphRAG.
Table 3: Categorizing representative retrievers used in GraphRAG.
**Method/Strategy** **Input** **Output** **Description**
Entity Linking Entity Mention Node Match query entity and graph node
Relational Ma... | {
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accurate entity recognition conducted by the query processor and the quality of labeled entities on
graph nodes. This technique is commonly applied in knowledge graphs, where Top-K nodes are
selected as starting points based on their textual similarity to the query. The similarity metric can
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**2.4.2** **Learning-based Retriever**
One significant limitation of heuristic-based retrievers is their over-reliance on pre-defined rules,
which limits their generalizability to data that does not strictly adhere to these rules. For example,
when confronted with entities that have slight semantic or structural varia... | {
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**x[l]i** [=][ γ][Θ]γ
**e[l]ij** [=][ γ][Θ]γ
x[l]i[−][1] _⊕_ � _ϕΘϕ_ �x[l]i[−][1], xj[l][−][1], eij� _,_ Node-level (3)
_j∈Ni_
**e[l]ij** [=][ γ][Θ]γ e[l]ij[−][1] _⊕_ � _ϕΘϕ_ �e[l]ij[−][1][,][ e]mn[l][−][1][,][ x][e]ij _[∩][e]mn�_ _,_ Edge-level (4)
_emn∈Nij[e]_
**G[l]** = ρΘρG ({x[l]i[,][ e][l]ij... | {
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**Integrated Retrieval: Integrated retrieval combines various types of retrievers to capture relevant**
information by balancing their strengths and weaknesses. Typically, integrated retrieval approaches
are categorized according to which individual retrievers are used in combination, with notable
examples including ne... | {
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field enlarges (i.e., the number of hops increases) in the retrieved subgraphs would also exponentially
increase the amount of context length in the prompt and dilute the focus of LLMs on the taskrelevant knowledge [358]. This poses a new requirement for the graph-based reranking mechanism to
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**2.5.3** **Graph Augmentation**
Graph augmentation aims to enrich the retrieved graph to either enhance the content or improve
the robustness of the generator. This process can involve adding supplementary information to the
retrieved graph, sourced from external data or knowledge embedded within LLMs. There are two
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decomposes the question, generates a reasoning trace, and identifies the specific knowledge needed
for the current step; and Summarization, where it summarizes the relevant knowledge from the
subgraph that retrieved based on the current reasoning trace.
**2.6** **Generator**
The generator aims to produce the desired ... | {
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**2.6.3** **Graph-based Generator**
In the scientific graph domain, GraphRAG generators often go beyond LLM-based methods due to
the need for accurate structure generation. RetMol [433] is particularly versatile because it can work
with various encoder and decoder architectures, supporting multiple generative models a... | {
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"... |
Note that the above-mentioned data structures can only represent basic graphs without support
for complex scenarios such as multi-relational edges or edge attributes. For instance, using an
adjacency matrix to represent a multi-relational attributed graph requires an expanded structure:
**A ∈** R[|V|×|V|×|R|], where R ... | {
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- Rule-based construction: Many traditional approaches use rule-based techniques for constructing
the graph. This takes the form of custom parsers and manually defined rules used to extract facts
from raw text. Note that these parsers can differ depending on the source of the text. Prominent
examples include ConceptNet... | {
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the entity is to the query. For Yasunaga et al. [493], if |V | > 200, they randomly sample 200
entities. Sun et al. [381] use a version of beam-search to explore the KG. Jiang et al. [185], Feng
et al. [110] extract all paths of length k ≤ 2 between seed entities. Alternatively, LARK [62]
retrieves all facts that lie o... | {
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representation based on the LLM-encoded query. They then run L rounds of message passing
where after each layer l only the top-K new edges are kept, resulting in a set of entities Cq[l] [. This]
is determined by a learnable attention weight, which prunes the other edges from the graph. The
final set of candidate entiti... | {
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**Other retrievers: KICGPT [439] is concerned with the task of knowledge graph completion, where**
given a partial fact (h, r, ∗), we want to predict the correct entity ˆe. KICGPT retrieves the entities
by first scoring all possible entities using a traditional KG embedding score function. That is, for a
score function... | {
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LLM is given the relational and textual information of each, and is asked to give it a score from 0
to 1, which is then used for re-ranking. GenTKG [246] orders the paths by the time they occurred,
further including the time with each. KICGPT [439] ranks all entities using the score of the KG
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- LangChain [8] is an open-source framework for using LLMs with various components and applications, including RAG, where using RAG on KGs is supportive.
### 4 Document Graph
A document graph typically models the connections between different documents or various granularity of documents. It is widely observed in rea... | {
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document-level relationships. Humans often consolidate scattered information into structured
knowledge to streamline the reasoning process and make more accurate judgments, in line with
cognitive load theory [384, 243]. Graph-based methods are well-suited for this task by constructing
word-level document graphs [307, 4... | {
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**– Sentence-Sentence edge: Sentence-sentence edges connect sentences based on their semantic**
similarity or relationships. For example, these edges can be constructed through sentence interactions [491, 284, 165], similarities between TF-IDF representations [237, 45], BM25 [297],
sentence embeddings [260, 410, 525], ... | {
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based on the local clustering coefficient, while Zhang et al. [539] employs path-centric pruning to
incorporate off-path information. Li et al. [229] dynamically drop irrelevant nodes during decoding,
and Angelova and Weikum [8] prune edges based on a similarity threshold. Additionally, Edge
et al. [98] uses community ... | {
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sentiment, quote attributions, and relations. These tools are valuable for constructing document
graphs.
- spaCy: [10] The spaCy is an advanced natural language processing library known for its speed
and neural network models, which are optimized for tasks such as tagging, parsing, named entity
recognition, text class... | {
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In recent years, significant progress has been made in the development of artificial intelligence
for science [1, 193, 393, 283]. Machine learning (ML) and deep neural network technologies are
increasingly driving scientific discovery from experimental data. Notably, generative models such
as Large Language Models (LLM... | {
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generated through depth-first search (DFS) on the molecular graph, following specific rules. 2D
molecular graphs represent atoms as nodes and bonds as edges, visually showing the connectivity of
atoms. 3D molecular graphs incorporate spatial coordinates for each atom, reflecting the molecule’s
structure in three-dimens... | {
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existing biomedical knowledge. This approach significantly enhances the transparency and
utility of predictive models. Delile et al. [78] map the text chunks to the knowledge graph, then
utilize graph distances to find the chunks most relevant to the user’s question. In addition, this
work introduces a scoring metric t... | {
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three categories according to the type of generated models they use: transformer-based generators,
diffusion model-based generators, and large language models-based generators.
- Transformer-based generator: RetMol [433] utilizes the Megatron version of the molecule
generative model Chemformer for drug discovery. Spec... | {
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"graph... |
- PubMed [269]: PubMed is a freely accessible database that primarily houses the MEDLINE
collection, containing references and abstracts in life sciences and biomedicine. Managed by
the United States National Library of Medicine (NLM) within the National Institutes of Health,
PubMed is part of the Entrez retrieval syst... | {
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**6.1** **Application Tasks**
- Entity Property Prediction: Entity property prediction focuses on predicting properties and
classifying categories for social entities in social networks, examples of which include the prediction
of partnership compatibility, assessment of morality, detection of account suspensions, ide... | {
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- Item-Item-Interaction [427, 528, 452]: This social graph captures interactions between items,
typically identified by shared interactions from the same customer or user. For example, a "co-view"
interaction between two products indicates that they are viewed by the same customer, while a
"view-add-to-cart" interactio... | {
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**6.4** **Organizer**
To further enhance the retrieved content, the organizer for the social graphs employs specialized
techniques beyond the typical re-ranking and filtering used in other graph domains [511, 152, 77].
For social graphs, the organizer of GraphRAG often uses Keyword Extraction, Profile Summarization,
a... | {
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**6.6.1** **Data Resources**
- STARK-Amazon[24] [452]: STARK-Amazon is a large-scale, semi-structured retrieval benchmark
dataset for product search on the Amazon platform, integrating textual and relational knowledge
bases. The nodes in the dataset represent products, colors, brands, and categories, while edges
captu... | {
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- Brexit[30] [565]: This dataset includes a portion of the X (Twitter) network, specifically the remainleave discourse before the 2016 UK Referendum on exiting the EU. It comprises a network with
7,589 users, 532,459 directed follow relationships, and 19,963 tweets, each associated with a binary
stance. The dataset is ... | {
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techniques [24, 437, 488] where each node represents a decision-making thinking step connected by
the reasoning flow. The dependency constraint and reasoning flow in the planning/reasoning graphs
can be naturally represented as relational knowledge, which forms the foundation for GraphRAG in
fulfilling planning/reasoni... | {
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- Inclusive Dependency [277]: The dependency described indicates that two connected nodes
belong to the same category or environment. For example, cobblestones and birdhouses are
both part of the category of garden decorations [427]. Hypergraphs can effectively capture such
belonging relationships where one entity belo... | {
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use a multi-round embedding similarity process integrated with reasoning steps, enhancing plan
fidelity. Moreover, reward-based retrieval involves a sophisticated search that further boosts accuracy.
Together, these high-quality strategies reduce the need for fine-grained reranking or filtering.
**7.5** **Generator**
... | {
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... |
retrieving movie details or recommending films, are grouped under the ’movie’ category, while
APIs focused on person-related tasks, like searching for actors, are classified under the ’person’
category. Additionally, if two APIs share a common parameter (e.g., movie-id), a link is established
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### 8 Tabular Graph
Tabular data is another type of structured data that is widely used in real-world applications [343] and
is typically stored in relational databases [66]. Tabular data may consist of a single table containing
samples and their attributes, or multiple tables that share primary and foreign keys. LLMs... | {
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use heuristics based on expert knowledge to establish connections [223]. Additionally, certain
approaches link features if they belong to the same instance [335].
- Instance-Feature graphs: The instance-feature graph is a heterogeneous graph, which connects
the instance with their corresponding features [135, 497, 411... | {
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44
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**8.5.1** **Data Resources**
Relational machine learning has recently gained popularity, leading to the development of several
benchmarks and datasets for tabular data. For example:
- RelBench [339, 115][45] is a collection of realistic, large-scale, and diverse benchmark datasets for
machine learning on relational d... | {
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preprocessing functions, including normalization, standardization, encoding of categorical features,
and dataloader preparation. Additionally, it includes a variety of tabular machine-learning models
and evaluation functions, making it a comprehensive tool for handling tabular data.
- DeepTables [179][53]: DeepTables ... | {
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"... |
- Communication Networks [113, 341]: A computer network system typically comprises two
interconnected graph layers: the logical and the physical. The logical layer represents the flow of
data, where traffic is routed through multiple intermediate routers before reaching its destination.
In this layer, nodes correspond ... | {
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node features are derived through principal component analysis (PCA), while cell node features are
obtained by aggregating the weighted features of the connected gene nodes. The most common way
to construct a single-cell graph is to build a KNN graph from single-cell data [414]. To be specific, the
data is first normal... | {
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**9.4** **Random Graph**
Random graphs are a foundational concept in network theory [304, 174] and are widely used for
modeling and studying complex networks in fields such as computer science, physics, biology,
and social sciences. They are constructed based on probabilistic rules, leading to various possible
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**10.2** **Retriever**
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text decoding process, no systematic study has yet demonstrated whether LLMs can distinguish
between these encodings and accurately recognize their corresponding geometric structures.
**10.5** **GraphRAG as a System**
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between structural integrity and the robustness of GraphRAG systems, paving the way for
robust performance in tasks requiring complex reasoning and planning.
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- End-to-End Benchmarks: To assess the system’s overall effectiveness, comprehensive end-to-end
benchmarks are essential. These should evaluate the quality of generated outputs, system response
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