DLSM: Distance Label Based Subgraph Matching on GPU

Abstract

Graphs have been prevalently used to represent complex data, such as social networks, citation networks and biological protein interaction networks. The subgraph matching problem has wide applications in the graph data computing area. Recently, many parallel matching algorithms have been proposed to speed up subgraph matching queries, among which the filter-join framework is attracting increasingly attentions in recent years. Existing filtering strategies are able to compress candidate vertex sets to a certain size. However, quite a few invalid vertices are still left, leading to unnecessary computation in later joining phases. We observed that the shortest distance between vertices can act as an important condition to further refine the candidate set. In this paper, we propose a method of shortest distance estimation based on the observation and design a new method based on distance coding. By this means we improve the efficiency of subgraph matching. The experimental results suggests that our method is more efficient and scalable than the state-of-the-art method.