{"type":"ScholarlyArticle","title":"Discrete Embedding for Latent Networks","authors":[{"name":"Hong Yang","slug":"hong-yang","given":"Hong","family":"Yang","affiliation":"Centre for Artificial Intelligence , University of Technology Sydney , Australia"},{"name":"Ling Chen","slug":"ling-chen","given":"Ling","family":"Chen","affiliation":"Centre for Artificial Intelligence , University of Technology Sydney , Australia"},{"name":"Minglong Lei","slug":"minglong-lei","given":"Minglong","family":"Lei","affiliation":"Faculty of Information Technology , Beijing University of Technology , China"},{"name":"Lingfeng Niu","slug":"lingfeng-niu","given":"Lingfeng","family":"Niu","affiliation":"School of Economics and Management , University of Chinese Academy of Sciences , China"},{"name":"Chuan Zhou","slug":"chuan-zhou","given":"Chuan","family":"Zhou","affiliation":"Academy of Mathematics and Systems Science , Chinese Academy of Sciences , China"},{"name":"Peng Zhang","slug":"peng-zhang","given":"Peng","family":"Zhang","affiliation":"Ant Financial Services Group , Hangzhou , China"}],"abstract":"Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks  DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.","doi":"10.24963/ijcai.2020/170","url":"https://ijcai.org/proceedings/2020/170","pdf":"https://ijcai.org/proceedings/2020/0170.pdf","markdown":"https://ijcai.org/proceedings/2020/170.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/170","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":"Data Mining","track":"Main track","number":170,"pages":{"first":1223,"last":1229},"keywords":["Machine Learning Applications: Networks","Data Mining: Mining Graphs, Semi Structured Data, Complex Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24409/file_24409.m3u8","length_sec":265,"provider":"hls"}],"references":[{"position":1,"raw":"[Atwood and Towsley, 2016] James Atwood and Don Towsley. 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