{"type":"ScholarlyArticle","title":"Effective Representing of Information Network by Variational Autoencoder","authors":[{"name":"Hang Li","slug":"hang-li","given":"Hang","family":"Li","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China"},{"name":"Haozheng Wang","slug":"haozheng-wang","given":"Haozheng","family":"Wang","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China"},{"name":"Zhenglu Yang","slug":"zhenglu-yang","given":"Zhenglu","family":"Yang","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China"},{"name":"Haochen Liu","slug":"haochen-liu","given":"Haochen","family":"Liu","affiliation":"Institute of Statistics , Nankai University , Tianjin , China"}],"abstract":"Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study proposes a deep network representation model that seamlessly integrates the text information and structure of a network. Our model captures highly non-linear relationships between nodes and complex features of a network by exploiting the variational autoencoder (VAE), which is a deep unsupervised generation algorithm. We also merge the representation learned with a paragraph vector model and that learned with the VAE to obtain the network representation that preserves both structure and text information. We conduct comprehensive empirical experiments on benchmark datasets and find our model performs better than state-of-the-art techniques by a large margin.","doi":"10.24963/ijcai.2017/292","url":"https://ijcai.org/proceedings/2017/292","pdf":"https://ijcai.org/proceedings/2017/0292.pdf","markdown":"https://ijcai.org/proceedings/2017/292.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/292","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":292,"pages":{"first":2103,"last":2109},"keywords":["Machine Learning: Structured Learning","Machine Learning: Deep Learning","Machine Learning: Data Mining","Natural Language Processing: Text Classification"],"videos":[],"references":[{"position":1,"raw":"[Belkin and Niyogi, 2001] Mikhail Belkin and Partha Niyogi. 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