CoDAR: Revealing the Generalized Procedure & Recommending Algorithms of Community Detection
Summary: CoDAR reveals a generalized procedure for community detection and tracks real-time structural changes during detection. It uses 12 metrics and a rating model to evaluate communities and recommend the best algorithm, with interactive visualization. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xiang Ying (Tsinghua University)
- 2. Chaokun Wang (Tsinghua University)
- 3. Meng Wang (Tsinghua University)
- 4. Jeffrey Xu Yu (Chinese University of Hong Kong)
- 5. Jun Zhang (Tsinghua University)
BibTeX Citation
@inproceedings{ying_sigmod16,
title = {{CoDAR: Revealing the Generalized Procedure \& Recommending Algorithms of Community Detection}},
author = {Ying, Xiang and Wang, Chaokun and Wang, Meng and Yu, Jeffrey Xu and Zhang, Jun},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2899386},
url = {https://dl.acm.org/doi/10.1145/2882903.2899386},
year = {2016}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,580 | Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework | 2015 | VLDB | 8.3887788e-05 |
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