{"type":"ScholarlyArticle","title":"A Fast Local Search Algorithm for Minimum Weight Dominating Set Problem on Massive Graphs","authors":[{"name":"Yiyuan Wang","slug":"yiyuan-wang","given":"Yiyuan","family":"Wang","affiliation":"School of Computer Science and Information Technology , Northeast Normal University , China; Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education , Jilin University , China"},{"name":"Shaowei Cai","slug":"shaowei-cai","given":"Shaowei","family":"Cai","affiliation":"State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences , China"},{"name":"Jiejiang Chen","slug":"jiejiang-chen","given":"Jiejiang","family":"Chen","affiliation":"School of Computer Science and Information Technology , Northeast Normal University , China; Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education , Jilin University , China"},{"name":"Minghao Yin","slug":"minghao-yin","given":"Minghao","family":"Yin","affiliation":"School of Computer Science and Information Technology , Northeast Normal University , China; Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education , Jilin University , China"}],"abstract":"The minimum weight dominating set (MWDS) problem is NP-hard and also important in many applications. Recent heuristic MWDS algorithms can hardly solve massive real world graphs effectively. In this paper, we design a fast local search algorithm called FastMWDS for the MWDS problem, which aims to obtain a good solution on massive graphs within a short time. In this novel local search framework, we propose two ideas to make it effective. Firstly, we design a new fast construction procedure with four reduction rules to cut down the size of massive graphs. Secondly, we propose the three-valued two-level configuration checking strategy to improve local search, which is interestingly a variant of configuration checking (CC) with two levels and multiple values. Experiment results on a broad range of massive real world graphs show that FastMWDS finds much better solutions than state of the art MWDS algorithms.","doi":"10.24963/ijcai.2018/210","url":"https://ijcai.org/proceedings/2018/210","pdf":"https://ijcai.org/proceedings/2018/0210.pdf","markdown":"https://ijcai.org/proceedings/2018/210.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/210","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Heuristic Search and Game Playing","track":"Main track","number":210,"pages":{"first":1514,"last":1522},"keywords":["Heuristic Search and Game Playing: Combinatorial Search and Optimisation","Heuristic Search and Game Playing: Heuristic Search"],"videos":[],"references":[{"position":1,"raw":"[Abramé et al., 2017] André Abramé, Djamal Habet, and Donia Toumi. 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