{"type":"ScholarlyArticle","title":"Modeling the Homophily Effect between Links and Communities for Overlapping Community Detection","authors":[{"name":"Hongyi Zhang","slug":"hongyi-zhang","given":"Hongyi","family":"Zhang","affiliation":""},{"name":"Tong Zhao","slug":"tong-zhao","given":"Tong","family":"Zhao","affiliation":""},{"name":"Irwin King","slug":"irwin-king","given":"Irwin","family":"King","affiliation":""},{"name":"Michael R. Lyu","slug":"michael-r-lyu","given":"Michael R.","family":"Lyu","affiliation":""}],"abstract":"Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated. Most of the approaches only view links as consequences of communities (community-to-link) but fail to explore how nodes' community memberships can be represented by their linked neighbors (link-to-community). In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities. From the community-to-link perspective, we apply a preference-based pairwise function by assuming that nodes with common communities have a higher probability to build links than those without common communities. From the link-to-community perspective, we propose a new community representation learning with network embedding by assuming that linked nodes have similar community representations. We conduct experiments on several real-world networks and the results show that our HNMF model is able to find communities with better quality compared with state-of-the-art baselines.","doi":null,"url":"https://ijcai.org/proceedings/2016/554","pdf":"https://ijcai.org/proceedings/2016/0554.pdf","markdown":"https://ijcai.org/proceedings/2016/554.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/554","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":554,"pages":{"first":3938,"last":3943},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}