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Local Graph Sparsification for Scalable Clustering

Summary: Local graph sparsification by per-node edge pruning uses a minhash-based similarity to retain the top neighbors per node. The approach delivers 10-50x speedups with negligible quality loss, and even improves clustering accuracy for some algorithms. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4488
Venue
SIGMOD
Year
2011
Pagerank
0.00011865557
Overall Rank
1,163 | 92.03%
DOI
10.1145/1989323.1989399

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{satuluri_sigmod11,
        title = {{Local Graph Sparsification for Scalable Clustering}},
        author = {Satuluri, Venu and Parthasarathy, Srinivasan and Ruan, Yiye},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989399},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989399},
        year = {2011}
}

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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
138 Discovering Large Dense Subgraphs in Massive Graphs 2005 VLDB 0.00029823423
3,450 Keyword Search on External Memory Data Graphs 2008 VLDB 7.4038805e-05
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