GitHub Stargazers: Graph Classification Dataset
A GitHub (stargazers network) dataset of 1,448,038 nodes and 2,985,781 edges across 12,725 graphs, collected by Rozemberczki et al. via GitHub API (2019). Each record contains graph ID, binary label (0=web development, 1=machine learning), developer-user follower edges. The dataset is notable for its graph classification dataset with 12,725 small graphs, each representing the social network of developers who starred a repository. Intended for research into graph classification, social network analysis, developer behavior modeling, and transfer learning.
Cite this dataset at
github-stargazers.echo.directory
Analytics
The full graph has 1,448,038 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Computed over the full corpus.
Record
- Catalog key
github-stargazers- Authors
- Rozemberczki, Benedek; Kiss, Oliver; Sarkar, Rik
- Year
- 2020
- Source paper
- Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs
- DOI / arXiv
- 10.1145/3340531.3412757
- License
- GNU GPL 3.0 (karateclub repository) (GPL-3.0-only)
- Records
- 1,448,038
- Nodes
- 1,448,038
- Edges
- 2,985,781
- Nodes are
- user / repository
- Edges are
- star
- Attributes
- nodes have attributes; edges have no attributes
- Languages
- en
- Collected
- 2019 (2010s)
- Tasks
- Graph classification inferred
From the graph: link prediction, community detection - Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://github.com/benedekrozemberczki/karateclub link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-09, 31.6 MB across 4 files. Checksums in MANIFEST.json.
Citations
Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs
Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
Journal Article · International Conference on Information and Knowledge Management · 2020
Cited by
- Multi-scale Attributed Node Embedding
Benedek Rozemberczki, Carl Allen et al. · J. Complex Networks · 2019 · cited 1.2K× - SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
Jun Xia, Li-Rong Wu et al. · The Web Conference · 2022 · cited 479× - Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
Benedek Rozemberczki, Rik Sarkar · International Conference on Information and Knowledge Management · 2020 · cited 454× - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Benedek Rozemberczki, Paul Scherer et al. · International Conference on Information and Knowledge Management · 2021 · cited 289× - From Local Structures to Size Generalization in Graph Neural Networks
Gilad Yehudai, Ethan Fetaya et al. · International Conference on Machine Learning · 2020 · cited 189×
Show all 10
- Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal et al. · International Conference on Machine Learning · 2024 · cited 124× - Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users
Ferenc B'eres, István András Seres et al. · IEEE International Conference on Decentralized Applications and Infrastructures · 2020 · cited 121× - Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings
Benedek Rozemberczki, Rik Sarkar · arXiv.org · 2021 · cited 108× - Automated Machine Learning on Graphs: A Survey
Ziwei Zhang, Xin Wang et al. · International Joint Conference on Artificial Intelligence · 2021 · cited 98× - On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights
Lingxiao Zhao, L. Akoglu · Big Data · 2020 · cited 94×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@inproceedings{Rozemberczki_2020,
series = {CIKM ’20},
title = {Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs},
url = {http://dx.doi.org/10.1145/3340531.3412757},
doi = {10.1145/3340531.3412757},
booktitle = {Proceedings of the 29th ACM International Conference on Information & Knowledge Management},
publisher = {ACM},
author = {Rozemberczki, Benedek and Kiss, Oliver and Sarkar, Rik},
year = {2020},
month = Oct,
pages = {3125–3132},
collection = {CIKM ’20}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Rozemberczki, B., Kiss, O., & Sarkar, R. (2020). GitHub Stargazers: Graph Classification Dataset [Data set]. https://doi.org/10.1145/3340531.3412757
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Rozemberczki, Benedek, et al. GitHub Stargazers: Graph Classification Dataset. 2020, doi.org/10.1145/3340531.3412757.