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Local Search for Maximum Vertex Weight Clique on Large Sparse Graphs with Efficient Data Structures

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AI 2016: Advances in Artificial Intelligence (AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9992))

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Abstract

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.

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Notes

  1. 1.

    https://github.com/Fan-Yi/Local-Search-for-Maximum-Vertex-Weight-Clique-on-Large-Sparse-Graphs-with-Efficient-Data-Structures.

  2. 2.

    http://www.graphrepository.com/networks.php.

  3. 3.

    In [27], both solvers are incorporated with a heuristic named BMS to solve large instances. For simplicity, we write them as MN/TS and LSCC for short.

  4. 4.

    For any vertices u and v, we use \(u = v\) to denote that u and v are the same vertex.

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Acknowledgment

We thank all anonymous reviewers for their valuable comments. This work is supported by ARC Grant FT0991785, NSF Grant No.61463044, NSFC Grant No.61572234 and Grant No.[2014]7421 from the Joint Fund of the NSF of Guizhou province of China.

We gratefully acknowledge the support of the Griffith University eResearch Services Team and the use of the High Performance Computing Cluster “Gowonda” to complete this research.

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Authors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia

    Yi Fan, Zongjie Ma, Lian Wen, Abdul Sattar & Kaile Su

  2. Department of Computer Science, Sun Yat-sen University, Guangzhou, China

    Chengqian Li

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  1. Yi Fan
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Correspondence to Yi Fan .

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Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Fan, Y., Li, C., Ma, Z., Wen, L., Sattar, A., Su, K. (2016). Local Search for Maximum Vertex Weight Clique on Large Sparse Graphs with Efficient Data Structures. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_21

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_21

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