{"type":"ScholarlyArticle","title":"Integrative Network Embedding via Deep Joint Reconstruction","authors":[{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Meng Ge","slug":"meng-ge","given":"Meng","family":"Ge","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Liang Yang","slug":"liang-yang","given":"Liang","family":"Yang","affiliation":"School of Computer Science and Engineering , Hebei University of Technology , Tianjin , China"},{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Longbiao Wang","slug":"longbiao-wang","given":"Longbiao","family":"Wang","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Weixiong Zhang","slug":"weixiong-zhang","given":"Weixiong","family":"Zhang","affiliation":"College of Math and Computer Science , Jianghan University , Wuhan , China; Department of Computer Science and Engineering , Washington University , St. Louis , USA"}],"abstract":"Network embedding is to learn a low-dimensional representation for a network in order to capture intrinsic features of the network. It has been applied to many applications, e.g., network community detection and user recommendation. One of the recent research topics for network embedding has been focusing on exploitation of diverse information, including network topology and semantic information on nodes of networks. However, such diverse information has not been fully utilized nor adequately integrated in the existing methods, so that the resulting network embedding is far from satisfactory. In this paper, we develop a weight-free multi-component network embedding approach by network reconstruction via a deep Autoencoder. Three key components make our new approach effective, i.e., a uniformed graph representation of network topology and semantic information, enhancement to the graph representation using local network structure (i.e., pairwise relationship on nodes) by sampling with latent space regularization, and integration of the diverse information in graph forms in a deep Autoencoder. Extensive experimental results on seven real-world networks demonstrate a superior performance of our method over nine state-of-the-art methods for embedding.","doi":"10.24963/ijcai.2018/473","url":"https://ijcai.org/proceedings/2018/473","pdf":"https://ijcai.org/proceedings/2018/0473.pdf","markdown":"https://ijcai.org/proceedings/2018/473.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/473","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":473,"pages":{"first":3407,"last":3413},"keywords":["Machine Learning Applications: Networks","Multidisciplinary Topics and Applications: AI and the Web"],"videos":[],"references":[{"position":1,"raw":"[Bernardes et al., 2015] Daniel Bernardes, Mamadou Diaby, Raphaël Fournier, Franc ¸oise FogelmanSoulié, and Em- manuel Viennet. 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