Facebook Pages — Company Category (Nov 2017)

A Facebook dataset of company page mutual-like edges from November 2017, collected by Rozemberczki et al. via the Facebook Graph API. Each record contains undirected edges between company pages that mutually liked each other, with node attributes including page name and category. undirected structure capturing symmetric inter-company endorsement relationships across multiple industries. Suitable for corporate social media behavior, community detection, and undirected social network analysis.

#Brand#Company#Facebook page#Page like

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Analytics

The interactive network needs JavaScript.

· graph.json

The full graph has 14,113 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.

14.1K
Nodes
52.3K
Edges
5.25e-04
Density
1
Components
0.01298
Assortativity
0.0%
Reciprocity
2.7K
Bridges
Most central nodes
83960.01524
119340.01502
8560.01403
113800.01375
98860.01311
82230.01268
12370.01212
89240.01134
18540.01042
110950.01027

Computed over the full corpus.

Record

Catalog key
fb-pages-company
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
14,113
Nodes
14,113
Edges
52,310
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-company.php link checked
Hosted copy
Mirrored on echo.directory on 2026-10-02, 1.0 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

376
citations
well cited
28
influential citations
7% of all citations

Cited by

Show all 10

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 — Company 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 — Company Category (Nov 2017). 2019, doi.org/10.1145/3341161.3342890.

BibTeX · RIS · CSL-JSON

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