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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
10310
Venue
VLDB
Year
2011
Pagerank
6.2821897e-05
Overall Rank
4,321 | 69.97%
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
202 Graph Indexing: A Frequent Structure-based Approach 2004 SIGMOD 0.00034881375
351 FG-Index: Towards Verification-Free Query Processing on Graph Databases 2007 SIGMOD 0.00026351742
1,720 Mining Significant Graph Patterns by Leap Search 2008 SIGMOD 0.00010757565
2,035 Generating Example Data for Dataflow Programs 2009 SIGMOD 9.7131385e-05
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