Google+ Social Network
A Google+ dataset of 211,187 users, 1,506,896 circle memberships, collected by Fire et al. via Google+ API (2013). Each record contains directed edges between Google+ users representing circle memberships (user → circle). Google+ social graph capturing directed circle relationships, where users add others to their circles, creating a directed social graph structure. Suitable for link prediction, social network analysis, community detection, circle-based social behavior analysis.
Cite this dataset at
google-plus.echo.directory
Analytics
The full graph has 211,187 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
google-plus- Authors
- Fire, M.; Tenenboim, R.; Puzis, R.; Lesser, O.; Rokach, L.; Elovici, Y.
- Year
- 2013
- Source paper
- Computationally Efficient Link Prediction in Variety of Social Networks
- DOI / arXiv
- 10.1145/2542182.2542192
- License
- See source page for citation/usage terms.
- Records
- 211,187
- Nodes
- 211,187
- Edges
- 1,506,896 (directed)
- Nodes are
- user
- Edges are
- circle membership
- Attributes
- nodes have no attributes; edges have no attributes
- Languages
- en
- Collected
- 2013 (2010s)
- Tasks
- From the graph: link prediction, community detection
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://networkrepository.com/soc-google-plus.php link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-09, 18.6 MB across 2 files. Checksums in MANIFEST.json.
Citations
Computationally efficient link prediction in a variety of social networks
M. Fire, L. Tenenboim-Chekina, Rami Puzis, O. Lesser, L. Rokach
Journal Article · ACM Transactions on Intelligent Systems and Technology · 2013
Cited by
- Stacking models for nearly optimal link prediction in complex networks
Amir Ghasemian, Homa Hosseinmardi et al. · Proceedings of the National Academy of Sciences of the United States of America · 2019 · cited 157× - Social networking data analysis tools & challenges
Androniki Sapountzi, K. Psannis · Future generations computer systems · 2016 · cited 149× - Progresses and challenges in link prediction
Tao Zhou · iScience · 2021 · cited 125× - Friend or foe? Fake profile identification in online social networks
M. Fire, Dima Kagan et al. · Social Network Analysis and Mining · 2013 · cited 114× - Organization Mining Using Online Social Networks
M. Fire, Rami Puzis et al. · Networks and Spatial Economics · 2013 · cited 98×
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- Path extension similarity link prediction method based on matrix algebra in directed networks
Feipeng Guo, Wei Zhou et al. · Computer Communications · 2022 · cited 87× - Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures
Guy Shtar, L. Rokach et al. · PLoS ONE · 2019 · cited 87× - Link prediction approach combined graph neural network with capsule network
Xiao-Yang Liu, Xiang Li et al. · Expert systems with applications · 2022 · cited 72× - Machine Learning in Network Centrality Measures
Felipe Grando, L. Granville et al. · ACM Computing Surveys · 2018 · cited 67× - Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data
David Toubiana, Rami Puzis et al. · Communications Biology · 2019 · cited 66×
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}
}
@article{Fire2013,
title = {Computationally Efficient Link Prediction in Variety of Social Networks},
author = {Fire, M. and Tenenboim, R. and Puzis, R. and Lesser, O. and Rokach, L. and Elovici, Y.},
journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
volume = {5},
number = {1},
pages = {10},
year = {2013},
publisher = {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
Fire, M., Tenenboim, R., Puzis, R., Lesser, O., Rokach, L., & Elovici, Y. (2013). Google+ Social Network [Data set]. https://doi.org/10.1145/2542182.2542192
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Fire, M., et al. Google+ Social Network. 2013, doi.org/10.1145/2542182.2542192.