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Mining Top-K Large Structural Patterns in a Massive Network

Summary: SpiderMine mines top-K patterns in a network with probability 1-ε. Abandoning edge-by-edge growth, it uses bounded-diameter spiders and a probabilistic framework to prune growth, trim combinatorics, and speed isomorphism via a multi-set spider encoding. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10500
Venue
VLDB
Year
2011
Pagerank
6.7842201e-05
Overall Rank
4,282 | 70.63%
DOI
10.14778/3402707.3402720

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhu_vldb11,
        title = {{Mining Top-K Large Structural Patterns in a Massive Network}},
        author = {Zhu, Feida and Qu, Qiang and Lo, David and Yan, Xifeng and Han, Jiawei and Yu, Philip S.},
        journal = {PVLDB},
        series = {{VLDB} '11},
        volume = {4},
        number = {11},
        pages = {807--818},
        doi = {10.14778/3402707.3402720},
        url = {https://doi.org/10.14778/3402707.3402720},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
177 Graph Indexing: A Frequent Structure-based Approach 2004 SIGMOD 0.00027100548
326 FG-Index: Towards Verification-Free Query Processing on Graph Databases 2007 SIGMOD 0.00021152829
1,972 Mining Significant Graph Patterns by Leap Search 2008 SIGMOD 9.3708222e-05
2,077 Generating Example Data for Dataflow Programs 2009 SIGMOD 9.2085989e-05
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