A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
It provides a comprehensive overview for researchers interested in computational graph problems, but it is incremental as a review paper.
This paper reviews the Maximum Clique Problem, covering classical algorithms, graph neural networks, and quantum algorithms, and concludes with benchmarks for testing these approaches.
This manuscript provides a comprehensive review of the Maximum Clique Problem, a computational problem that involves finding subsets of vertices in a graph that are all pairwise adjacent to each other. The manuscript covers in a simple way classical algorithms for solving the problem and includes a review of recent developments in graph neural networks and quantum algorithms. The review concludes with benchmarks for testing classical as well as new learning, and quantum algorithms.