Mining top-K large structural patterns in a massive network

Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Gabrielle Dawn Allen

Research output: Contribution to journalArticlepeer-review


With ever-growing popularity of social networks, web and bio-networks, mining large frequent patterns from a single huge network has become increasingly important. Yet the existing pattern mining methods cannot offer the efficiency desirable for large pattern discovery. We propose Spider- Mine, a novel algorithm to efficiently mine top-K largest frequent patterns from a single massive network with any user-specified probability of 1-ε. Deviating from the existing edge-by-edge (i.e., incremental) pattern-growth framework, SpiderMine achieves its efficiency by unleashing the power of small patterns of a bounded diameter, which we call "spiders". With the spider structure, our approach adopts a probabilistic mining framework to find the top-k largest patterns by (i) identifying an affordable set of promising growth paths toward large patterns, (ii) generating large patterns with much lower combinatorial complexity, and finally (iii) greatly reducing the cost of graph isomorphism tests with a new graph pattern representation by a multi-set of spiders. Extensive experimental studies on both synthetic and real data sets show that our algorithm outperforms existing methods.

Original languageEnglish (US)
Pages (from-to)807-818
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number11
StatePublished - Aug 2011

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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