Random Walk in Large Real-World Graphs for Finding Smaller Vertex Cover
MetadataShow full item record
The problem of finding a minimum vertex cover (MinVC) in a graph is a prominent NP-hard problem of great importance in both theory and application. During recent decades, there has been much interest in finding optimal or near-optimal solutions to this problem. Many existing heuristic algorithms for MinVC are based on local search strategies. Recently, an algorithm called FastVC takes a first step towards solving the MinVC problem for large real-world graphs. However, FastVC may be trapped by local minima during the local search stage due to the lack of suitable diversification mechanisms. In this work, we design a new random walk strategy to help FastVC escape from local minima. Experiments conducted on a broad range of large real-world graphs show that our algorithm outperforms state-of-the-art algorithms on most classes of the benchmark and finds smaller vertex covers on a considerable portion of the graphs.
Proceedings: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI 2016)
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Artificial Intelligence and Image Processing not elsewhere classified