Multi-SPMiner: A Deep Learning Framework for Multi-Graph Frequent Pattern Mining with Application to spatiotemporal Graphs

Abstract

Mining frequent patterns in multigraphs is a challenging task in graph analysis with numerous real-world applications. This paper introduces a novel framework for frequent pattern mining on multi-graphs using the multi-SPMiner method. The approach is inspired by SPMiner, which was the first approach to employ deep learning in graph motif mining tasks. Multi-SPMiner builds on this foundation and focuses on the extraction of frequent motifs in single multi-graphs, specifically spatiotemporal graphs. Multi-SPMiner employs a two-step approach to extract the most frequent motifs in a graph with a high support value. In the first step, it embeds the nodes into an embedding order space, and in the second step, it performs a walk in the space to obtain the frequent motifs by iteratively growing the motif starting from a single node. The results obtained highlight the effectiveness of the proposed approach in identifying frequent motifs in single multigraphs, which is a crucial task in many real-world applications. Moreover, we demonstrate that our method is a generalization of SPMiner by testing it on single connection graphs.