Parallel top-k subgraph query in massive graphs: Computing from the perspective of single vertex

Abstract

In the real world, many problems on massive graphs can be mapped to an underlying critical problem of discovering top-k subgraphs. For massive graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only top-k matches are desired. Meanwhile, parallel algorithm is urgent for the scalability of massive graph computing. In this paper, we address the challenges of top-k subgraph query in massive graph. Firstly, we present a new graph matching notion: “approximate graph simulation”. With approximate graph simulation, top-k subgraph query can be customized by appointing a weighted query graph, which provides good flexibility for different application scenarios. Secondly, we propose a parallel top-k subgraph query algorithm at the level of vertex. With such algorithm, each vertex in massive graph obtains its matching state separately without requiring global graph information. In the algorithm, we also design a filter mechanism to speed up the the computation and a aggregation mechanism to obtain top-k vertices for query focus. Using real-life datasets, we experimentally verify that our approach of parallel top-k subgraph query are efficient.