{"type":"ScholarlyArticle","title":"Adversarial Mutual Information Learning for Network Embedding","authors":[{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Lu Zhai","slug":"lu-zhai","given":"Lu","family":"Zhai","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Zhigang Li","slug":"zhigang-li","given":"Zhigang","family":"Li","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"School of Artificial Intelligence , Hebei University of Technology , Tianjin , China"},{"name":"Liang Yang","slug":"liang-yang","given":"Liang","family":"Yang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Yuxiao Huang","slug":"yuxiao-huang","given":"Yuxiao","family":"Huang","affiliation":"Data Science , George Washington University , Washington , D.C. , USA"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"Department of Computer Science , University of Illinois at Chicago , Chicago , IL , USA; Institute for Data Science , Tsinghua University , Beijing , China"}],"abstract":"Network embedding which is to learn a low dimensional representation of nodes in a network has been used in many network analysis tasks. Some network embedding methods, including those based on generative adversarial networks (GAN) (a promising deep learning technique), have been proposed recently. Existing GAN-based methods typically use GAN to learn a Gaussian distribution as a priori for network embedding. However, this strategy makes it difficult to distinguish the node representation from Gaussian distribution. Moreover, it does not make full use of the essential advantage of GAN (that is to adversarially learn the representation mechanism rather than the representation itself), leading to compromised performance of the method. To address this problem, we propose to use the adversarial idea on the representation mechanism, i.e. on the encoding mechanism under the framework of autoencoder. Specifically, we use the mutual information between node attributes and embedding as a reasonable alternative of this encoding mechanism (which is much easier to track). Additionally, we introduce another mapping mechanism (which is based on GAN) as a competitor into the adversarial learning system. A range of empirical results demonstrate the effectiveness of the proposed approach.","doi":"10.24963/ijcai.2020/459","url":"https://ijcai.org/proceedings/2020/459","pdf":"https://ijcai.org/proceedings/2020/0459.pdf","markdown":"https://ijcai.org/proceedings/2020/459.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/459","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning Applications","track":"Main track","number":459,"pages":{"first":3321,"last":3327},"keywords":["Multidisciplinary Topics and Applications: Web Analysis of Communities","Machine Learning Applications: Networks","Machine Learning: Adversarial Machine Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25373/file_25373.m3u8","length_sec":287,"provider":"hls"}],"references":[{"position":1,"raw":"[Cao et al., 2015] Shaosheng Cao, Wei Lu, and Qiongkai Xu. 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