Graph Pattern Matching: From Intractable to Polynomial Time

Wenfei Fan, Jianzhong Li, Shuai Ma, Nan Tang, Yinghui Wu, Yunpeng Wu

Research output: Contribution to journalArticlepeer-review

Abstract

Graph pattern matching is typically defined in terms of sub-graph isomorphism, which makes it an np-complete problem. Moreover, it requires bijective functions, which are often too restrictive to characterize patterns in emerging applications. We propose a class of graph patterns, in which an edge denotes the connectivity in a data graph within a predefined number of hops. In addition, we define matching based on a notion of bounded simulation, an extension of graph simulation. We show that with this revision, graph pattern matching can be performed in cubic-time, by providing such an algorithm. We also develop algorithms for incrementally finding matches when data graphs are updated, with performance guarantees for dag patterns. We experimentally verify that these algorithms scale well, and that the revised notion of graph pattern matching allows us to identify communities commonly found in real-world networks.
Original languageEnglish
Pages (from-to)264-275
Number of pages12
JournalProceedings of the VLDB Endowment (PVLDB)
Volume3
Issue number1
Publication statusPublished - 2010

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