Harnessing the mathematics of matrix decomposition to solve planted and maximum clique problem

TL;DR

This paper introduces a novel matrix decomposition approach using weighted ℓ₁-norm and ADMM to accurately identify maximum cliques in graphs, with proven convergence and superior performance on synthetic and real data.

Contribution

It proposes a new mathematical model for maximum clique detection based on matrix decomposition with weighted ℓ₁-norm and provides theoretical guarantees and an efficient ADMM algorithm.

Findings

Outperforms existing models on planted clique problems

Successfully recovers cliques in real-world graphs

Provides theoretical convergence and recovery guarantees

Abstract

We consider the problem of identifying a maximum clique in a given graph. We have proposed a mathematical model for this problem. The model resembles the matrix decomposition of the adjacency matrix of a given graph. The objective function of the mathematical model includes a weighted $\ell_{1}$-norm of the sparse matrix of the decomposition, which has an advantage over the known $\ell_{1}-$norm in reducing the error. The use of dynamically changing the weights for the $\ell_{1}$-norm has been motivated. We have used proximal operators within the iterates of the ADMM (alternating direction method of multipliers) algorithm to solve the optimization problem. Convergence of the proposed ADMM algorithm has been provided. The theoretical guarantee of the maximum clique in the form of the low-rank matrix has also been established using the golfing scheme to construct approximate dual certificates. We have constructed conditions that guarantee the recovery and uniqueness of the solution, as well as a tight bound on the dual matrix that validates optimality conditions. Numerical results for planted cliques are presented showing clear advantages of our model when compared with two recent mathematical models. Results are also presented for randomly generated graphs with minimal errors. These errors are found using a formula we have proposed based on the size of the clique. Moreover, we have applied our algorithm to real-world graphs for which cliques have been recovered successfully. The validity of these clique sizes comes from the decomposition of input graph into a rank-one matrix (corresponds to the clique) and a sparse matrix.