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README.md

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-license: cc-by-4.0
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+license: cc-by-4.0
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+# Dataset Card for CrediBench 1.1
+
+<!-- Provide a quick summary of the dataset. -->
+
+Information regarding the CrediBench 1.1, a large-scale, temporal webgraph constituted of web data pulled from [Common Crawl](https://commoncrawl.org/overview). 
+A prior version of the paper is [available here](https://arxiv.org/abs/2509.23340) (NPGML workshop @ NeurIPS 2025), with the latest version still under review. 
+CrediBench 1.0, presented in this prior work, constituted of a static webgraph with 1 month's data, while the current version contains 3 months of data (October to December 2024, surrounding the U.S Federal elections, a period of increased misinformation). 
+
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+This dataset is composed of monthly slices of large-scale web networks. These webgraphs contain 1+ billion edges, and 45+ million nodes per month.
+In these webgraphs, the nodes represent a website domain (e.g, `google.com`) and an edge represents a directed hyperlink relation (e.g, an edge from `cbc.ca` to `reuters.com` indicates that a page on `cbc.ca`'s website contains a hyperlink to a `reuters.com` page).
+These webgraphs are supplemented with text attributes, partly from Common Crawl and from web scraping, as text features play an important role in misinformation detection. 
+Additionally, we supplement them with credibility scores as made available by [Lin et al.](https://github.com/hauselin/domain-quality-ratings/tree/main/data), to enable supervised and semi-supervised learning as explained in our paper. 
+
+- **Curated by** a team of collaborators from the Complex Data Lab @ Mila - Quebec AI Institute, the University of Oxford, McGill University, Concordia University, UC Berkeley, University of Montreal, and AITHYRA.
+- **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant:
+*Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI
+Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada.
+- **License:** CC-BY-4.0 (as retributed from Common Crawl). 
+
+### Resources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **[Repository](https://github.com/ekmpa/CrediGraph)** 
+- **[Paper](https://arxiv.org/abs/2509.23340)**
+- **[Common Crawl](https://commoncrawl.org/overview)** is our primary data source, supplemented with web scraping and multiple datasets for credibility signals:
+  - [DQR](https://github.com/hauselin/domain-quality-ratings/tree/main/data) for credibility scores for supervised learning, and
+  - [Yasin et al.](https://doi.org/10.1016/j.dib.2023.109959)'s phishing domains,
+  - [Potpelwar et al.](https://doi.org/10.1016/j.dib.2025.111972)'s malware domains, and
+  - [Aung et al.](https://dl.acm.org/doi/10.1145/3486622.3493983)'s legitimate domains, for semi-supervised learning.
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+This dataset is intended as a data source for research efforts against misinformation online. Specifically, as the first large-scale, text-attributed webgraph that is also dynamic,
+CrediBench stands as an ideal data source for efforts to develop methods for unreliable domain detection based on spatio-temporal cues. 
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+This dataset is not intended for LLM training. Designed for the goal of misinformation detection at the domain level and web scale, this dataset contains numerous 
+domains and content pages that contain innapropriate content which may be harmful if used for training conversational AI, or other types of generative AI outside the scope of our task. 
+
+
+### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+The process of collection, processing and use is detailed in our team's paper. We collect data through our proposed CrediBench pipeline (available at our repository), 
+which builds a month's worth of data by pulling from Common Crawl, builds the graph from it and processes it to discard isolated and low-degree nodes.
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```
+@article{kondrupsabry2025credibench,
+  title={{CrediBench: Building Web-Scale Network Datasets for Information Integrity}},
+  author={Kondrup, Emma and Sabry, Sebastian and Abdallah, Hussein and Yang, Zachary and Zhou, James and Pelrine, Kellin and Godbout, Jean-Fran{\c{c}}ois and Bronstein, Michael and Rabbany, Reihaneh and Huang, Shenyang},
+  journal={arXiv preprint arXiv:2509.23340},
+  year={2025},
+  note={New Perspectives in Graph Machine Learning Workshop @ NeurIPS 2025},
+  url={https://arxiv.org/abs/2509.23340}
+}
+```
+
+**APA:**
+
+```
+Kondrup, E., Sabry, S., Abdallah, H., Yang, Z., Zhou, J., Pelrine, K., Godbout, J.-F., Bronstein, M., Rabbany, R., & Huang, S. (2025).
+CrediBench: Building Web-Scale Network Datasets for Information Integrity.
+New Perspectives in Graph Machine Learning Workshop @ NeurIPS 2025. arXiv:2509.23340. https://arxiv.org/pdf/2509.23340
+```
+
+## Dataset Card Authors / Contact
+
+For any questions on the dataset, please contact [Emma Kondrup](mailto:emma.kondrup@mila.quebec), [Sebastian Sabry](mailto:sebastian.sabry@mcgill.ca), or [Shenyang (Andy) Huang](mailto:shenyang.huang@mail.mcgill.ca).