Hypergraph matching based on Marginalized Constrained Compatibility

Jiang Su, Le Dong, Peng Ren, Edwin R. Hancock

Abstract: We aim to match two hypergraphs via pairwise characterization of multiple relationships. To this end, we introduce a technique referred to as Marginalized Constrained Compatibility Estimation (MCCE), which transforms the compatibility tensor representing hyper-edge similarities into a compatibility matrix representing edge similarities. We then cluster graph vertices associated with the compatibility matrix and extract its dominant set as the optimal matches. Our MCCE-based method overcomes the information loss arising in arithmetic average, which is commonly used for marginal-ization in the hypergraph matching literature. Experiments demonstrate the effectiveness of our method.