FRISSMiner: Mining Frequent Graph Sequence Patterns Induced by Vertices

Abstract

The mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences (dynamic graphs or evolving graphs). In this paper, we define a novel subgraph subsequence class called an “induced subgraph subsequence” to enable the efficient mining of a complete set of frequent patterns from graph sequences containing large graphs and long sequences. We also propose an efficient method for mining frequent patterns, called “FRISSs (Frequent Relevant, and Induced Subgraph Subsequences)”, from graph sequences. The fundamental performance of the method is evaluated using artificial datasets, and its practicality is confirmed through experiments using a real-world dataset.