Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)
We present a new energy-minimization framework for the graph isomorphism problem which is based on an equivalent maximum clique formulation. The approach is centered around a fundamental result proved by Motzkin and Straus in the mid-1960s, and recently expanded in various ways, which allows us to formulate the maxi(cid:173) mum clique problem in terms of a standard quadratic program. To solve the program we use "replicator" equations, a class of simple continuous- and discrete-time dynamical systems developed in var(cid:173) ious branches of theoretical biology. We show how, despite their inability to escape from local solutions, they nevertheless provide experimental results which are competitive with those obtained us(cid:173) ing more elaborate mean-field annealing heuristics.