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COMMIT: A Scalable Approach to Mining Communication Motifs from Dynamic Networks

Summary: COMMIT scales mining of communication motifs in dynamic networks by turning evolving graphs into a sequence database and pruning the search. Up to 100x speedups vs baselines; motifs reveal recurring interaction patterns and social-network influence. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5067
Venue
SIGMOD
Year
2015
Pagerank
7.4144782e-05
Overall Rank
3,439 | 76.41%
DOI
10.1145/2733272.2737791

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gurukar_sigmod15,
        title = {{COMMIT: A Scalable Approach to Mining Communication Motifs from Dynamic Networks}},
        author = {Gurukar, Saket and Ranu, Sayan and Ravindran, Balaraman},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2733272.2737791},
        url = {https://dl.acm.org/doi/10.1145/2733272.2737791},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,085 GraMI: Frequent Subgraph and Pattern Mining in a Single Large Graph 2014 VLDB 0.0001225302
1,115 Comparing Stars: On Approximating Graph Edit Distance 2009 VLDB 0.00012117375
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