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

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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 difficult combinatorial optimization problem and is called the maximum clique problem. In this paper, we formulate a very convenient continuous characterization of the maximum clique problem based on the symmetric rank-one non-negative 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 the DIMACS 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.

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Acknowledgements

This research was conducted when the first author was visiting the Department of Mathematics and Operational Research at the University of Mons. We would like to thank the reviewers and Prof. Kunal Narayan Chaudhury for their insightful feedback that helped us improve the paper significantly. NG acknowledges the support of the F.R.S–FNRS (incentive Grant for Scientific Research No. F.4501.16) and of the ERC (Starting Grant No. 679515).

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Correspondence to Nicolas Gillis.

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Communicated by Jim Luedtke.

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Belachew, M.T., Gillis, N. Solving the Maximum Clique Problem with Symmetric Rank-One Non-negative Matrix Approximation. J Optim Theory Appl 173, 279–296 (2017). https://doi.org/10.1007/s10957-016-1043-6

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