Local Search for Maximum Vertex Weight Clique on Large Sparse Graphs with Efficient Data Structures
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The Maximum Vertex Weight Clique (MVWC) problem is a generalization of the Maximum Clique problem, which exists in many real-world applications. However, it is NP-hard and also very difficult to approximate. In this paper we developed a local search MVWC solver to deal with large sparse instances. We first introduce random walk into the multi-neighborhood greedy search, and then implement the algorithm with efficient data structures. Experimental results showed that our solver significantly outperformed state-of-the-art local search MVWC solvers. It attained all the best-known solutions, and found new best-known solutions on some instances.
AI 2016: Advances in Artificial Intelligence. 29th Australasian Joint Conference Hobart, TAS, Australia, December 5–8, 2016 Proceedings
Artificial Intelligence and Image Processing not elsewhere classified