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PRS: Parallel Relaxation Simulation for Massive Graphs

Graph pattern matching is becoming important for a variety of emerging applications such as social network analysis. Graph pattern matching is traditionally defined in terms of subgraph isomorphism or graph simulation. These notions, however, often impose too strong constraint to identify meaningful matches. In this paper, we propose a new graph pattern matching based on a notion of relaxation simulation, which extends graph simulation by allowing partially absent vertices. We show that relaxation simulation is able to find significant matches which traditional approaches of graph pattern matching fail to catch. We propose two parallel algorithms to apply relaxation simulation to massive graph since the graph in practice is considerably large. The algorithms are based on Bulk Synchronous Parallel model and can be easily deployed on cloud computing platforms. Finally, we experimentally verify the effectiveness and efficiency of these algorithms, using real-life data and synthesis data. The results suggest that relaxation simulation is a promising framework for real-life massive graph analysis.