Graph Matching using Adjacency Matrix Markov Chains

This paper describes a spectral method for graph-matching. We adopt a graphical models viewpoint in which the graph adjacency matrix is taken to represent the transition probability matrix of a Markov chain. The nodeorder of the steady state random walk associated with this Markov chain is determined by the co-efficent order of the leading eigenvector of the adjacency matrix. We match nodes in different graphs by aligning their sequence order in the steady-state walk. The method proceeds from the nodes with the largest leading eigenvector co-efficient. We develop a brushfire search method to assign correspondences between nodes using the rank-order of the eigenvector co-efficients in first-order n eighbourhoods of the graphs. We demonstrate the utility of the new graph-matching method on both synthetic and real graphs.

• Type: Conference
• Year: 2001
• Where: BMVC
• Authors: Antonio Robles-Kelly, Edwin R. Hancock
• Keywords: Adjacency Matrix, BMVC 2001, Computer Vision, Leading Eigenvector, Markov Chain