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An Efficient Algorithm for Distance-based Structural Graph Clustering

Summary: Proposes distance-based SCAN: similarity = ratio of neighbors within d; non-neighbors and edge weights influence clustering. Uses bottom-k sketches (ADS) for O(k) similarity estimates, with histogram pruning for scalable, approximate clustering. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6610
Venue
SIGMOD
Year
2023
Pagerank
5.2675506e-05
Overall Rank
9,444 | 35.21%
DOI
10.1145/3588725

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{liu_sigmod23,
        title = {{An Efficient Algorithm for Distance-based Structural Graph Clustering}},
        author = {Liu, Kaixin and Wang, Sibo and Zhang, Yong and Xing, Chunxiao},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588725},
        url = {https://dl.acm.org/doi/10.1145/3588725},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,448 Efficient Influential Community Search over Dynamic Graphs 2026 SIGMOD 5.093636e-05
10,528 Effective Durable Community Search in Large Temporal Graph 2026 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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