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CSV: Visualizing and Mining Cohesive Subgraphs

Summary: CSV proposes an approximate algorithm mapping edges and nodes into a multi-dimensional space to visualize cohesive subgraphs; dense regions reveal cohesive components. With worst-case O(V^2 log V) for fixed dimension (often sub-quadratic in practice), it enables visualization and pre-filtering to scale exact miners like CLAN. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4069
Venue
SIGMOD
Year
2008
Pagerank
7.0665158e-05
Overall Rank
3,862 | 73.51%
DOI
10.1145/1376616.1376663

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod08,
        title = {{CSV: Visualizing and Mining Cohesive Subgraphs}},
        author = {Wang, Nan and Parthasarathy, Srinivasan and Tan, Kian-Lee and Tung, Anthony K. H.},
        series = {{SIGMOD} '08},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1376616.1376663},
        url = {https://dl.acm.org/doi/10.1145/1376616.1376663},
        year = {2008}
}

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