Math Overflow Temporal Network — C2A
A Math Overflow (C2A) dataset of 13,840 users and 81,121 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (c2a) between users with Unix timestamps. The dataset is notable for its C2A interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
Cite this dataset at
mathoverflow-c2a.echo.directory
Analytics
The full graph has 13,840 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
mathoverflow-c2a- Authors
- Paranjape, Ashwin; Benson, Austin R.; Leskovec, Jure
- Year
- 2017
- Source paper
- Motifs in Temporal Networks
- DOI / arXiv
- 10.1145/3018661.3018731
- License
- Academic use (SNAP Stanford) (LicenseRef-Research-Only)
- Records
- 13,840
- Nodes
- 13,840
- Edges
- 81,121 (directed)
- Nodes are
- user
- Edges are
- comment to answer
- Attributes
- nodes have no attributes; edges have attributes
- Languages
- en
- Collected
- 2350 days (~6.4 years)
- Tasks
- From the graph: link prediction, community detection
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://snap.stanford.edu/data/sx-mathoverflow.html link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-09, 4.0 MB across 1 files. Checksums in MANIFEST.json.
Citations
Motifs in Temporal Networks
Ashwin Paranjape, Austin R. Benson, J. Leskovec
Book · Web Search and Data Mining · 2016
Cited by
- Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
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Q. F. Lotito, F. Musciotto et al. · Communications Physics · 2021 · cited 172× - Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction
Sheng Xiang, Dawei Cheng et al. · International Conference on Information and Knowledge Management · 2022 · cited 153× - A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
Lilapati Waikhom, Ripon Patgiri · Artificial Intelligence Review · 2022 · cited 129×
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- A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges
ZhengZhao Feng, Rui Wang et al. · IEEE Transactions on Knowledge and Data Engineering · 2024 · cited 97× - Group interactions modulate critical mass dynamics in social convention
Iacopo Iacopini, G. Petri et al. · Communications Physics · 2021 · cited 86× - Co-citation and Co-authorship Networks of Statisticians
Pengsheng Ji, Jia-Shun Jin et al. · Journal of business & economic statistics · 2021 · cited 85× - Higher-order Structure Based Anomaly Detection on Attributed Networks
Xu Yuan, Naqin Zhou et al. · 2021 IEEE International Conference on Big Data (Big Data) · 2021 · cited 81× - Temporal Link Prediction: A Unified Framework, Taxonomy, and Review
Meng Qin, Dit-Yan Yeung · ACM Computing Surveys · 2022 · cited 78×
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{paranjape2017motifs,
title = {Motifs in Temporal Networks},
author = {Paranjape, Ashwin and Benson, Austin R. and Leskovec, Jure},
booktitle = {WSDM},
year = {2017},
doi = {10.1145/3018661.3018731}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Paranjape, A., Benson, A. R., & Leskovec, J. (2017). Math Overflow Temporal Network — C2A [Data set]. https://doi.org/10.1145/3018661.3018731
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Paranjape, Ashwin, et al. Math Overflow Temporal Network — C2A. 2017, doi.org/10.1145/3018661.3018731.