{"type":"ScholarlyArticle","title":"Learning Network Embedding with Community Structural Information","authors":[{"name":"Yu Li","slug":"yu-li","given":"Yu","family":"Li","affiliation":"College of Computer Science and Technology , Jilin University , Changchun , China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , Jilin University , Changchun , China"},{"name":"Ying Wang","slug":"ying-wang","given":"Ying","family":"Wang","affiliation":"College of Computer Science and Technology , Jilin University , Changchun , China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , Jilin University , Changchun , China"},{"name":"Tingting Zhang","slug":"tingting-zhang","given":"Tingting","family":"Zhang","affiliation":"School of Statistics , Jilin University of Finance and Economics , Changchun , China"},{"name":"Jiawei Zhang","slug":"jiawei-zhang","given":"Jiawei","family":"Zhang","affiliation":"IFM Lab , Department of Computer Science , Florida State University , Tallahassee FL , USA"},{"name":"Yi Chang","slug":"yi-chang","given":"Yi","family":"Chang","affiliation":"School of Artificial Intelligence , Jilin University , Changchun , China"}],"abstract":"Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including network reconstruction, link prediction and vertex classification.","doi":"10.24963/ijcai.2019/407","url":"https://ijcai.org/proceedings/2019/407","pdf":"https://ijcai.org/proceedings/2019/0407.pdf","markdown":"https://ijcai.org/proceedings/2019/407.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/407","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":407,"pages":{"first":2937,"last":2943},"keywords":["Machine Learning: Relational Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Adamic and Glance, 2005] Lada A Adamic and Natalie Glance. 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