Multiple Pattern Graph Correlations for Efficient Graph Pattern Matching

Jing Yu, Xiaomei Liu, Yanbing Liu, Yue Hu

Abstract: 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.