There is a growing interest in designing systems for graph pattern mining in recent years. The existing systems mostly focus on small patterns and have difficulty in mining larger patterns. 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 . The experimental results show that Angelica achieves significant speedups against the state-of-the-art graph pattern mining systems and supports large pattern mining that none of the existing systems can handle.
Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications. Numerous systems have been proposed for FSM in the past decade. Although these systems show good performance for small patterns (with no more than four vertices), we found that they have difficulty in mining larger patterns. 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 reduces the overhead of isomorphism checks, and a subgraph sampling technique that mitigates the issue of subgraph explosion . The experimental results show that our system achieves significant speedups against the state-of-the-art graph pattern mining systems and supports larger pattern mining tasks that none of the existing systems can handle.
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There is a growing interest in designing systems for graph pattern mining in recent years. The existing systems mostly focus on small patterns and
Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications. Numerous systems have been proposed for FSM in the past decade. Although these systems show good performance for small patterns (with no more than four vertices), we found that they
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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
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
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addresses the issue of expensive isomorphism check, and approximate join
reduces the overhead of isomorphism checks, and a subgraph sampling technique
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of large patterns
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Angelica
our system
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large pattern mining
larger pattern mining tasks