{"type":"ScholarlyArticle","title":"Recommending Links to Maximize the Influence in Social Networks","authors":[{"name":"Federico Corò","slug":"federico-coro","given":"Federico","family":"Corò","affiliation":"Gran Sasso Science Institute , L'Aquila , Italy"},{"name":"Gianlorenzo D'Angelo","slug":"gianlorenzo-d-angelo","given":"Gianlorenzo","family":"D'Angelo","affiliation":"Gran Sasso Science Institute , L'Aquila , Italy"},{"name":"Yllka Velaj","slug":"yllka-velaj","given":"Yllka","family":"Velaj","affiliation":"CWI , Amsterdam , Netherlands; ISI Foundation , Turin , Italy"}],"abstract":"Social link recommendation systems, like \"People-you-may-know\" on Facebook, \"Who-to-follow\" on Twitter, and \"Suggested-Accounts\" on Instagram assist the users of a social network in establishing new connections with other users. While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim at predicting users' behavior, they accelerate the creation of links that are likely to be created in the future, and, as a consequence, they reinforce social biases by suggesting few (popular) users, while giving few chances to the majority of users to build new connections and increase their popularity.In this paper we measure the popularity of a user by means of its social influence, which is its capability to influence other users' opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a constant factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections. We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence.","doi":"10.24963/ijcai.2019/304","url":"https://ijcai.org/proceedings/2019/304","pdf":"https://ijcai.org/proceedings/2019/0304.pdf","markdown":"https://ijcai.org/proceedings/2019/304.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/304","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":"Machine Learning A-L","track":"Main track","number":304,"pages":{"first":2195,"last":2201},"keywords":["Machine Learning Applications: Networks","Multidisciplinary Topics and Applications: Social Sciences","Machine Learning: Recommender Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Adamic and Adar, 2003] Lada A. 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