Multiple Pattern Graph Correlations for Efficient Graph Pattern Matching

Graph pattern matching has wide applications in social network analysis, such as identifying important communities, social roles and hidden behavior structures. Existing algorithms are mainly designed for single-pattern tasks, where patterns are processed sequentially and independently. However, many scenarios require multiple patterns to be processed as a batch, where single-pattern scheme will cost much redundant computation caused by matching similar sub-structures in the pattern set. Therefore, the sequential graph pattern matching scheme is not always the most efficient. This paper aims to propose a multiple pattern graph optimization algorithm for the subgraph isomorphism task. We comprehensively study the structural correlations among the multiple patterns and represent them by a compact tree-structured index. To support fast insertion and deletion of the pattern index, we present a dynamic updating algorithm to avoid index reconstruction from scratch. Based on the index, an efficient matching algorithm is proposed to answer multiple patterns in a heuristic scheduling order and avoid redundant computation. Extensive experiments on real and synthetic datasets prove that our solution is several times faster comparing with the state-of-the-art work.