FCSG-Miner: Frequent closed subgraph mining in multi-graphs

Abstract

Graph data-based mining is vital in various fields, such as business management, chemistry, and social networks. Frequency-based frameworks have limitations regarding large mining output. In many real-world scenarios, not all frequent patterns hold significant meaning. To address this issue, concise representation such as closed patterns is proposed. Unfortunately, existing methods utilize support or occurrence as frequency measurements, which have drawbacks. Support overlooks the isomorphic quantity of a single graph, while occurrence lacks the downward closure property. In this paper, we introduce an approach for mining frequent closed subgraphs. A novel measure MMNI (multiple minimum node image) is introduced to strike a balance between support and occurrence measures. Additionally, we design a novel structure to store occurrence information. We develop a pruning strategy and employ an early termination strategy to enhance efficiency. To evaluate the performance of our algorithm, we conduct experiments on seven real datasets, considering four aspects. The results demonstrate that our algorithm has high efficiency and performance compared to the state-of-the-art algorithm for closed subgraph mining. In several cases, our method requires only 50% of the time consumed by previous approaches. We also present a real-world application example in the domain of bike-sharing systems.