Discovery of Correlated Sequential Subgraphs from a Sequence of Graphs
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In recent years, the mining of a complete set 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. 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.