{"type":"ScholarlyArticle","title":"Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation","authors":[{"name":"Yi Fan","slug":"yi-fan","given":"Yi","family":"Fan","affiliation":"Department of Computer Science , Jinan University , Guangzhou , China; Institute for Integrated and Intelligent Systems , Griffith University , Brisbane , Australia"},{"name":"Nan Li","slug":"nan-li","given":"Nan","family":"Li","affiliation":"Department of Computer and Information Sciences , Temple University , Philadelphia , USA"},{"name":"Chengqian Li","slug":"chengqian-li","given":"Chengqian","family":"Li","affiliation":"Department of Computer Science , Sun Yat-sen University , Guangzhou , China"},{"name":"Zongjie Ma","slug":"zongjie-ma","given":"Zongjie","family":"Ma","affiliation":"Institute for Integrated and Intelligent Systems , Griffith University , Brisbane , Australia"},{"name":"Longin Jan Latecki","slug":"longin-jan-latecki","given":"Longin Jan","family":"Latecki","affiliation":"Department of Computer and Information Sciences , Temple University , Philadelphia , USA"},{"name":"Kaile Su","slug":"kaile-su","given":"Kaile","family":"Su","affiliation":"Department of Computer Science , Jinan University , Guangzhou , China; Institute for Integrated and Intelligent Systems , Griffith University , Brisbane , Australia"}],"abstract":"The Maximum Vertex Weight Clique (MVWC) problem is NP-hard and also important in real-world applications. In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will restart. In addition, when the local search has no other options except dropping vertices, it will use random walk. Experimental results show that our solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Also it is the unique solver which is comparable with state-of-the-art methods on both BHOSLIB and large crafted graphs. Furthermore we evaluated our solver in clustering aggregation.  Experimental results on a number of real data sets demonstrate that our solver outperforms the state-of-the-art for solving the derived MVWC problem and helps improve the final clustering results.","doi":"10.24963/ijcai.2017/87","url":"https://ijcai.org/proceedings/2017/87","pdf":"https://ijcai.org/proceedings/2017/0087.pdf","markdown":"https://ijcai.org/proceedings/2017/87.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/87","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Constraints and Satisfiability","track":"Main track","number":87,"pages":{"first":622,"last":630},"keywords":["Combinatorial & Heuristic Search: Heuristic Search","Constraints and Satisfiability: Constraint Optimisation","Machine Learning: Data Mining","Combinatorial & Heuristic Search: Combinatorial search/optimisation"],"videos":[],"references":[{"position":1,"raw":"[Brendel and Todorovic, 2010] William Brendel and Sinisa Todorovic. 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