Mining Frequent Subgraph by Incidence Matrix Normalization

Abstract

Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain welllabeled vertices and edges. However, there are a number of applications that lead to graphs that do not share these characteristics, for which these algorithms highly become inefficient. In this paper we propose a fast algorithm for mining frequent subgraphs in large database of labeled graphs. The algorithm uses incidence matrix to represent the labeled graphs and to detect their isomorphism. Starting from the frequent edges from the graph database, the algorithm searches the frequent subgraphs by adding frequent edges progressively. By normalizing the incidence matrix of the graph, the algorithm can effectively reduce the computational cost on verifying the isomorphism of the subgraphs. Experimental results show that the algorithm has higher speed and efficiency than that of other similar ones.