ISSN : 1796-203X
Volume : 3    Issue : 10    Date : October 2008

Mining Frequent Subgraph by Incidence Matrix Normalization
Jia Wu and Ling Chen
Page(s): 109-115
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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.

Index Terms
graph; incidence matrix; isomorphism; data mining