Solving the Maximum Clique Problem with Symmetric Rank-One Nonnegative Matrix Approximation

TL;DR

This paper introduces a continuous optimization approach for the maximum clique problem using symmetric rank-one nonnegative matrix approximation, establishing a correspondence between stationary points and graph cliques, and demonstrating improved algorithm performance.

Contribution

It presents a novel continuous formulation for MCP, linking stationary points to cliques, and develops an efficient algorithm that outperforms existing methods.

Findings

The new algorithm outperforms existing algorithms based on Motzkin-Straus formulation.

Local minima correspond to maximal cliques, and global minima to maximum cliques.

The approach is effective on synthetic and real datasets.

Abstract

Finding complete subgraphs in a graph, that is, cliques, is a key problem and has many real-world applications, e.g., finding communities in social networks, clustering gene expression data, modeling ecological niches in food webs, and describing chemicals in a substance. The problem of finding the largest clique in a graph is a well-known NP-hard problem and is called the maximum clique problem (MCP). In this paper, we formulate a very convenient continuous characterization of the MCP based on the symmetric rank-one nonnegative approximation of a given matrix, and build a one-to-one correspondence between stationary points of our formulation and cliques of a given graph. In particular, we show that the local (resp. global) minima of the continuous problem corresponds to the maximal (resp. maximum) cliques of the given graph. We also propose a new and efficient clique finding algorithm based on our continuous formulation and test it on various synthetic and real data sets to show that the new algorithm outperforms other existing algorithms based on the Motzkin-Straus formulation, and can compete with a sophisticated combinatorial heuristic.