Efficient graph edit distance computation using isomorphic vertices

Abstract

In this paper, we study the problem of graph edit distance (GED) computation. We empirically observe that real graph data contain many isomorphic substructures, which incur redundant computation. Based on this observation, we aim at reducing the cost of GED computation by avoiding redundant computation caused by isomorphic substructures. To detect isomorphic substructures, we precisely define a notion of vertex isomorphism and propose a dynamic programming algorithm that identifies isomorphic vertices in a graph. By taking advantage of isomorphic vertices, we develop an efficient GED computation algorithm. In experiments, we show that isomorphic vertices are effectively used in reducing the search space of GED computation, and as a result our approach improves the performance of GED computation.