Local Search with Noisy Strategy for Minimum Vertex Cover in Massive Graphs
Finding minimum vertex covers (MinVC) for simple undirected graphs is a well-known NP-hard problem. In the literature there have been many heuristics for obtaining good vertex covers. However, most of them focus on solving this problem in relatively small graphs. Recently, a local search solver called FastVC is designed to solve the MinVC problem on real-world massive graphs. Since the traditional best-picking heuristic was believed to be of high complexity, FastVC replaces it with an approximate best-picking strategy. However, since best-picking has been proved to be powerful for a wide range of problems, abandoning it may be a great sacrifice. In this paper we have developed a local search MinVC solver which utilizes best-picking with noise to remove vertices. Experiments conducted on a broad range of real-world massive graphs show that our proposed method finds better vertex covers than state-of-the-art local search algorithms on many graphs.
KeywordsMinimum vertex cover Heuristic search Massive graphs Combinatorial optimization Social networks
This work is supported by ARC Grant FT0991785, NSF Grant No. 61463044 and Grant No. 7421 from the Joint Fund of the NSF of Guizhou province of China.
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