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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
10309
Venue
VLDB
Year
2011
Pagerank
6.2839861e-05
Overall Rank
4,330 | 69.88%
DOI
-

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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
203 Graph Indexing: A Frequent Structure-based Approach 2004 SIGMOD 0.00034889335
350 FG-Index: Towards Verification-Free Query Processing on Graph Databases 2007 SIGMOD 0.00026365067
1,747 Mining Significant Graph Patterns by Leap Search 2008 SIGMOD 0.00010691242
2,035 Generating Example Data for Dataflow Programs 2009 SIGMOD 9.7149269e-05
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