Before revision:  In this work, we propose Angelica, a single-machine graph pattern mining system aiming at supporting large patterns. We first propose a new computation model called multi-vertex exploration . The model allows us to divide a large pattern mining task into smaller matching tasks. Different from the existing systems which perform vertex-by-vertex exploration , we explore larger subgraphs by joining small subgraphs. Based on the new computation model, we further enhance the performance through an index-based quick pattern technique that addresses the issue of expensive isomorphism check, and approximate join that mitigates the issue of subgraph explosion of large patterns .
After revision:  In this work, we propose a novel two-vertex exploration strategy to accelerate the mining process. Compared with the single-vertex exploration adopted by previous systems, our two-vertex exploration avoids the large memory consumption issue and significantly reduces the memory access overhead. We further enhance the performance through an index-based quick pattern technique that addresses the issue of expensive isomorphism check, and approximate join that mitigates the issue of subgraph explosion of large patterns .