Exploiting Vertex Relationships in Speeding up Subgraph Isomorphism over Large Graphs

Abstract

Subgraph Isomorphism is a fundamental problem in graph data processing. Most existing subgraph isomorphism algo-rithms are based on a backtracking framework which com-putes the solutions by incrementally matching all query ver-tices to candidate data vertices. However, we observe that extensive duplicate computation exists in these algorithms, and such duplicate computation can be avoided by exploit-ing relationships between data vertices. Motivated by this, we propose a novel approach, BoostIso, to reduce duplicate computation. Our extensive experiments with real datasets show that, after integrating our approach, most existing subgraph isomorphism algorithms can be speeded up sig-nificantly, especially for some graphs with intensive vertex relationships, where the improvement can be up to several orders of magnitude