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Frequent Subgraph Mining (FSM) is the key task in many graph mining andmachine learning applications. Numerous systems have been proposed for FSM inthe past decade. Although these systems show good performance for smallpatterns (with no more than four vertices), we found that they have difficultyin mining larger patterns. In this work, we propose a novel two-vertexexploration strategy to accelerate the mining process. Compared with thesingle-vertex exploration adopted by previous systems, our two-vertexexploration avoids the large memory consumption issue and significantly reducesthe memory access overhead. We further enhance the performance through anindex-based quick pattern technique that reduces the overhead of isomorphismchecks, and a subgraph sampling technique that mitigates the issue of subgraphexplosion. The experimental results show that our system achieves significantspeedups against the state-of-the-art graph pattern mining systems and supportslarger pattern mining tasks that none of the existing systems can handle.

Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration