Feasibility of Graph Sequence Mining based on Admissibility Constraints

Abstract

In recent years, the mining of frequent subgraphs from labeled graph data has been extensively studied. However, to our best knowledge, almost no methods have been proposed to find frequent subsequences of graphs from a set of graph sequences where the numbers of vertices and edges increase or decrease. In this paper, we define a novel class of graph subsequences by introducing axiomatic rules of graph transformation, their admissibility constraints and a union graph. Then we propose an efficient approach named "GTRACE" to enumerate frequent transformation subsequences (FTSs) of graphs from a given set of graph sequences. Its fundamental performance has been evaluated by using artificial datasets, and its practicality has been confirmed through the experiments using real world datasets.