Solving the Maximum Clique Problem with Symmetric Rank-One Nonnegative Matrix Approximation
This work offers a new continuous optimization approach for the NP-hard maximum clique problem, benefiting researchers in combinatorial optimization and network analysis.
The paper introduces a continuous formulation of the maximum clique problem using symmetric rank-one nonnegative matrix approximation, establishing a one-to-one correspondence between stationary points and cliques. The proposed algorithm outperforms Motzkin-Straus-based methods and competes with combinatorial heuristics on synthetic and real datasets.
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.