Facebook Pages — Sport Category (Nov 2017)
A Facebook dataset of 13,866 pages, 86,858 mutual likes, collected by Rozemberczki et al. via the Facebook Graph API (November 2017). Each record contains undirected edges between blue verified Facebook sport pages that mutually liked each other, with node attributes including page name and raw Facebook ID. Graph of blue verified Facebook sport pages, where edges represent mutual likes between pages, capturing how fans engage with sports teams, athletes, and sporting content. Suitable for community detection in social networks, graph embedding, sports fan engagement analysis, team rivalry studies, sports media and fandom network analysis.
Cite this dataset at
fb-pages-sport.echo.directory
Analytics
The full graph has 13,866 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
fb-pages-sport- Authors
- Rozemberczki, Benedek; Davies, Ryan; Sarkar, Rik; Sutton, Charles
- Year
- 2019
- Source paper
- GEMSEC: Graph Embedding with Self Clustering
- DOI / arXiv
- 10.1145/3341161.3342890
- License
- GNU GPL 3.0 (GEMSEC release; Network Repository copy) (GPL-3.0-only)
- Records
- 13,866
- Nodes
- 13,866
- Edges
- 86,858
- Nodes are
- facebook page
- Edges are
- mutual like
- Attributes
- nodes have attributes; edges have no attributes
- Languages
- en
- Collected
- 2017-11 (2010s)
- Tasks
- From the graph: link prediction, community detection
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://networkrepository.com/fb-pages-sport.php link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-16, 1.4 MB across 3 files. Checksums in MANIFEST.json.
Citations
GEMSEC: Graph Embedding with Self Clustering
Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton
Book · International Conference on Advances in Social Networks Analysis and Mining · 2018
Cited by
- Multi-scale Attributed Node Embedding
Benedek Rozemberczki, Carl Allen et al. · J. Complex Networks · 2019 · cited 1.2K× - 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× - Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines Chami, S. Abu-el-haija et al. · Journal of machine learning research · 2020 · cited 352× - A Time-Dependent SIR Model for COVID-19 With Undetectable Infected Persons
Yi-Cheng Chen, Ping-En Lu et al. · IEEE Transactions on Network Science and Engineering · 2020 · cited 232× - Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs
Benedek Rozemberczki, Oliver Kiss et al. · International Conference on Information and Knowledge Management · 2020 · cited 222×
Show all 10
- A Comprehensive Review of Community Detection in Graphs
Jiakang Li, Song-Ning Lai et al. · Neurocomputing · 2023 · cited 122× - Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
Jing-Ya Zhou, Ling Liu et al. · ACM Computing Surveys · 2021 · cited 115× - Influence maximization in social networks using transfer learning via graph-based LSTM
Sanjay Kumar, Abhishek Mallik et al. · Expert systems with applications · 2022 · cited 110× - Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings
Benedek Rozemberczki, Rik Sarkar · arXiv.org · 2021 · cited 108× - Survey on graph embeddings and their applications to machine learning problems on graphs
I. Makarov, Dmitrii Kiselev et al. · PeerJ Computer Science · 2021 · cited 107×
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{rozemberczki2019gemsec,
title = {GEMSEC: Graph Embedding with Self Clustering},
author = {Rozemberczki, Benedek and Davies, Ryan and Sarkar, Rik and Sutton, Charles},
booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2019},
pages = {65-72},
year = {2019},
organization = {ACM}
}
@inproceedings{nr,
title = {The Network Data Repository with Interactive Graph Analytics and Visualization},
author = {Ryan A. Rossi and Nesreen K. Ahmed},
booktitle = {AAAI},
url = {https://networkrepository.com},
year = {2015}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Rozemberczki, B., Davies, R., Sarkar, R., & Sutton, C. (2019). Facebook Pages — Sport Category (Nov 2017) [Data set]. https://doi.org/10.1145/3341161.3342890
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Rozemberczki, Benedek, et al. Facebook Pages — Sport Category (Nov 2017). 2019, doi.org/10.1145/3341161.3342890.