Graph dissimilarity (or similarity) computation is one of the key intermediate operations for intelligent information processing. In this paper, a new dissimilarity measure is proposed to gauge the pairwise structural difference between graphs through constrained graph matching. The measure is obtained as a solution to an optimization problem that we formalize by a new objective function in matrix representation, based on the adjacency matrices of graphs and binary constraints. An EM-type algorithm based on the minimum-weight bipartite graph method is proposed to optimize the matrix in polynomial time. The suitability of the proposed measure is evaluated in nearest-neighbor classification on four real-world graph datasets and on synthetic datasets.
['Lifei Chen']
EM-type method for measuring graph dissimilarity