{"type":"ScholarlyArticle","title":"Pivotal Relationship Identification: The K-Truss Minimization Problem","authors":[{"name":"Weijie Zhu","slug":"weijie-zhu","given":"Weijie","family":"Zhu","affiliation":"East China Normal University , China; Zhejiang Lab , Hangzhou , China"},{"name":"Mengqi Zhang","slug":"mengqi-zhang","given":"Mengqi","family":"Zhang","affiliation":"Zhejiang Gongshang University , China"},{"name":"Chen Chen","slug":"chen-chen","given":"Chen","family":"Chen","affiliation":"East China Normal University , China"},{"name":"Xiaoyang Wang","slug":"xiaoyang-wang","given":"Xiaoyang","family":"Wang","affiliation":"Zhejiang Gongshang University , China"},{"name":"Fan Zhang","slug":"fan-zhang","given":"Fan","family":"Zhang","affiliation":"The University of New South Wales , Australia"},{"name":"Xuemin Lin","slug":"xuemin-lin","given":"Xuemin","family":"Lin","affiliation":"East China Normal University , China; Zhejiang Lab , Hangzhou , China; The University of New South Wales , Australia"}],"abstract":"In a social network, the strength of relationships between users can significantly affect the stability of the network. In this paper, we use the k-truss model to measure the stability of a social network. To identify critical connections, we propose a novel problem, named k-truss minimization. Given a social network G and a budget b, it aims to find b edges for deletion which can lead to the maximum number of edge breaks in the k-truss of G. We show that the problem is NP-hard. To accelerate the computation, novel pruning rules are developed to reduce the candidate size. In addition, we propose an upper bound based strategy to further reduce the searching space. Comprehensive experiments are conducted over real social networks to demonstrate the efficiency and effectiveness of the proposed techniques.","doi":"10.24963/ijcai.2019/677","url":"https://ijcai.org/proceedings/2019/677","pdf":"https://ijcai.org/proceedings/2019/0677.pdf","markdown":"https://ijcai.org/proceedings/2019/677.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/677","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":677,"pages":{"first":4874,"last":4880},"keywords":["Multidisciplinary Topics and Applications: Social Sciences","Multidisciplinary Topics and Applications: Databases","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Akbas and Zhao, 2017] Esra Akbas and Peixiang Zhao. 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