{"type":"ScholarlyArticle","title":"Improving Information Centrality of a Node in Complex Networks by Adding Edges","authors":[{"name":"Liren Shan","slug":"liren-shan","given":"Liren","family":"Shan","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing , Fudan University , China; School of Computer Science , Fudan University , China"},{"name":"Yuhao Yi","slug":"yuhao-yi","given":"Yuhao","family":"Yi","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing , Fudan University , China; School of Computer Science , Fudan University , China"},{"name":"Zhongzhi Zhang","slug":"zhongzhi-zhang","given":"Zhongzhi","family":"Zhang","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing , Fudan University , China; School of Computer Science , Fudan University , China"}],"abstract":"The problem of increasing the centrality of a network node arises in many practical applications. In this paper, we study the optimization problem of maximizing the information centrality Iv of a given node v in a network with n nodes and m edges, by creating k new edges incident to v. Since Iv is the reciprocal of the sum of resistance distance Rv between v and all nodes, we alternatively consider the problem of minimizing Rv by adding k new edges linked to v. We show that the objective function is monotone and supermodular. We provide a simple greedy algorithm with an approximation factor (1 − 1/e) and O(n^3) running time. To speed up the computation, we also present an algorithm to compute (1 − 1/e − epsilon) approximate resistance distance Rv after iteratively adding k edges, the running time of which is Otilde(mk*epsilon^−2) for any epsilon > 0, where the Otilde(·) notation suppresses the poly(log n) factors. We experimentally demonstrate the effectiveness and efficiency of our proposed algorithms.","doi":"10.24963/ijcai.2018/491","url":"https://ijcai.org/proceedings/2018/491","pdf":"https://ijcai.org/proceedings/2018/0491.pdf","markdown":"https://ijcai.org/proceedings/2018/491.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/491","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":"Machine Learning Applications","track":"Main track","number":491,"pages":{"first":3535,"last":3541},"keywords":["Constraints and SAT: Constraint Optimisation","Machine Learning Applications: Networks","Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Social Sciences"],"videos":[],"references":[{"position":1,"raw":"[Avrachenkov and Litvak, 2006] K. 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