This paper examines the problem of approximate graph matching (isomorphism). Given graphs G, H with p nodes, represented by respective adjacency matrices A, B, Find a permutation matrix P that best "matches" AP and PB.
This paper poses the multimodal graph matching problem as a convex optimization problem, and solves it using augmented Langrangian techniques (viz., ADMM). This is an important problem with application in several fields. Experimental results on synthetic and multiple real world datasets demonstrate effectiveness of the proposed approach.