Wikipedia Requests for Adminship Signed Network
A Wikipedia (online encyclopedia) dataset of 11,377 users and 196,614 directed signed votes (143,879 support, 40,667 oppose, 12,068 neutral) with 189,541 vote rationale texts, collected by West and Leskovec from Wikipedia's public adminship election records spanning 2003–2013, distributed by SNAP. Each record contains directed signed edges from voter to candidate: +1 support, -1 oppose, 0 neutral; election outcome per candidate (0/1); natural language vote rationale text per vote. The dataset is notable for its unique combination of signed network structure with natural language rationale text per edge, enabling joint graph and text analysis; one of the few real-world signed networks with explicit textual justifications for positive and negative links. Intended for research into signed network analysis, trust and reputation modeling, text-attributed graph research, opinion mining, link sign prediction with text features, and community detection in collaborative platforms.
Cite this dataset at
wiki-rfa.echo.directory
Analytics
The full graph has 11,377 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Topic 0
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
Topic 6
Topic 7
Topic 8
Topic 9
Topic 10
Topic 11
Show all 19 topics
Topic 12
Topic 13
Topic 14
Topic 15
Topic 16
Topic 17
Topic 18
Sampled: text metrics from 50K of 189.5K records; topic model from 20K.
Record
- Catalog key
wiki-rfa- Authors
- West, Robert; Leskovec, Jure
- Year
- 2014
- Source paper
- How Users Weigh Their Options on the Wikipedia Requests for Adminship
- DOI / arXiv
- 10.1145/2566486.2567975
- License
- Unknown — SNAP public research dataset
- Records
- 189,541
- Nodes
- 11,377
- Edges
- 188,077 (directed)
- Nodes are
- editor
- Edges are
- signed vote
- Attributes
- nodes have no attributes; edges have attributes
- Text corpus
- vote rationale text
- Languages
- en
- Collected
- 2003 to 2013 (2000s, 2010s)
- Tasks
- Edge sign prediction (binary) inferred
From the graph: link prediction, community detection - Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://snap.stanford.edu/data/wiki-RfA.html link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 47.8 MB across 1 files. Checksums in MANIFEST.json.
Citations
Comment-based multi-view clustering of web 2.0 items
Xiang-Nan He, Min-Yen Kan, Pei-Chu Xie, Xiao Chen
Book · The Web Conference · 2014
Cited by
- Neural Collaborative Filtering
Xiang-Nan He, Lizi Liao et al. · The Web Conference · 2017 · cited 7.4K× - Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Xiang-Nan He, Hanwang Zhang et al. · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2016 · cited 1.1K× - Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Jun Xiao, Hao Ye et al. · International Joint Conference on Artificial Intelligence · 2017 · cited 1K× - Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
Yongfeng Zhang, Guo-Kun Lai et al. · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2014 · cited 877× - TriRank: Review-aware Explainable Recommendation by Modeling Aspects
Xiang-Nan He, Tao Chen et al. · International Conference on Information and Knowledge Management · 2015 · cited 561×
Show all 10
- A Comprehensive Survey on Multi-View Clustering
Uno Fang, Man Li et al. · IEEE Transactions on Knowledge and Data Engineering · 2023 · cited 318× - BiRank: Towards Ranking on Bipartite Graphs
Xiang-Nan He, Ming Gao et al. · IEEE Transactions on Knowledge and Data Engineering · 2017 · cited 180× - Low-rank kernel learning for graph-based clustering
Zhao Kang, Liang-Jian Wen et al. · Knowledge-Based Systems · 2019 · cited 164× - Multi-view clustering via pairwise sparse subspace representation
Qi-Yue Yin, Shu Wu et al. · Neurocomputing · 2015 · cited 138× - Deep Learning for Matching in Search and Recommendation
Jun Xu, Xiang-Nan He et al. · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2018 · cited 125×
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{west2014wikipedia,
title = {How Users Weigh Their Options on the Wikipedia Requests for Adminship},
author = {West, Robert and Leskovec, Jure},
booktitle = {Proceedings of the 23rd International World Wide Web Conference (WWW)},
year = {2014},
publisher = {ACM},
doi = {10.1145/2566486.2567975}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
West, R., & Leskovec, J. (2014). Wikipedia Requests for Adminship Signed Network [Data set]. https://doi.org/10.1145/2566486.2567975
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
West, Robert, and Jure Leskovec. Wikipedia Requests for Adminship Signed Network. 2014, doi.org/10.1145/2566486.2567975.
