Replicator Equations, Maximal Cliques, and Graph Isomorphism
Abstract
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.
Cite
Text
Pelillo. "Replicator Equations, Maximal Cliques, and Graph Isomorphism." Neural Information Processing Systems, 1998.Markdown
[Pelillo. "Replicator Equations, Maximal Cliques, and Graph Isomorphism." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/pelillo1998neurips-replicator/)BibTeX
@inproceedings{pelillo1998neurips-replicator,
title = {{Replicator Equations, Maximal Cliques, and Graph Isomorphism}},
author = {Pelillo, Marcello},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {550-556},
url = {https://mlanthology.org/neurips/1998/pelillo1998neurips-replicator/}
}