{"type":"ScholarlyArticle","title":"Finding Communities with Hierarchical Semantics by Distinguishing General and Specialized topics","authors":[{"name":"Ge Zhang","slug":"ge-zhang","given":"Ge","family":"Zhang","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Jian Gao","slug":"jian-gao","given":"Jian","family":"Gao","affiliation":"College of Information Science and Technology , Dalian Maritime University , Dalian , China"},{"name":"Pengfei Jiao","slug":"pengfei-jiao","given":"Pengfei","family":"Jiao","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Françoise Fogelman-Soulié","slug":"francoise-fogelman-soulie","given":"Françoise","family":"Fogelman-Soulié","affiliation":"School of Computer Software , Tianjin University , Tianjin , China"},{"name":"Xin Huang","slug":"xin-huang","given":"Xin","family":"Huang","affiliation":"Department of Computer Science , Hong Kong Baptist University , Hong Kong , China"}],"abstract":"Using network topology and semantic contents to find topic-related communities is a new trend in the field of community detection. By analyzing texts in social networks, we find that topics in networked contents are often hierarchical. In most cases, they have a two-level semantic structure with general and specialized topics, to respectively denote common and specific interests of communities. However, the existing community detection methods ignore such a hierarchy and take all words used to describe node semantics from an identical perspective. This indiscriminate use of words leads to natural defects in depicting networked content in which the deep semantics is not fully utilized. To address this problem, we propose a novel probabilistic generative model. By distinguishing the general and specialized topics of words, our model not only can find community structures more accurately, but also provide two-level semantic interpretation for each community. We train the model by deriving an efficient inference method under the framework of variational expectation-maximization. We provide a case study to show the ability of our algorithm in deep semantic interpretability of communities. The superiority of our algorithm for community detection is further demonstrated in comparison with eight state-of-the-art algorithms on eight real-world networks.","doi":"10.24963/ijcai.2018/507","url":"https://ijcai.org/proceedings/2018/507","pdf":"https://ijcai.org/proceedings/2018/0507.pdf","markdown":"https://ijcai.org/proceedings/2018/507.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/507","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":507,"pages":{"first":3648,"last":3654},"keywords":["Machine Learning Applications: Networks","Multidisciplinary Topics and Applications: AI and the Web"],"videos":[],"references":[{"position":1,"raw":"[Balasubramanyan and Cohen, 2011] Ramnath Bal- asubramanyan and Willim W.Cohen. 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