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Mining Statistically Significant Connected Subgraphs in Vertex Labeled Graphs

Summary: Mining statistically significant connected subgraphs in vertex-labeled graphs via chi-square; supports discrete and multi-dimensional continuous labels. Edge contraction creates a super-graph to prune the search; dense graphs yield few super-vertices, sparse graphs trade accuracy for speed, achieving ~96% of optimal chi-square and scalable on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4841
Venue
SIGMOD
Year
2014
Pagerank
5.6664362e-05
Overall Rank
7,231 | 50.39%
DOI
10.1145/2588555.2588574

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{arora_sigmod14,
        title = {{Mining Statistically Significant Connected Subgraphs in Vertex Labeled Graphs}},
        author = {Arora, Akhil and Sachan, Mayank and Bhattacharya, Arnab},
        series = {{SIGMOD} '14},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2588555.2588574},
        url = {https://dl.acm.org/doi/10.1145/2588555.2588574},
        year = {2014}
}

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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
1,972 Mining Significant Graph Patterns by Leap Search 2008 SIGMOD 9.3708222e-05
8,087 Mining Statistically Significant Substrings using the Chi-Square Statistic 2012 VLDB 5.4904274e-05
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