{"type":"ScholarlyArticle","title":"Discrete Network Embedding","authors":[{"name":"Xiaobo Shen","slug":"xiaobo-shen","given":"Xiaobo","family":"Shen","affiliation":"‡ School of Computer Science and Engineering , Nanyang Technological University Centre for Artificial Intelligence , FEIT , University of Technology Sydney"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"School of Computer and Engineering , Nanjing University of Science and Technology"},{"name":"Weiwei Liu","slug":"weiwei-liu","given":"Weiwei","family":"Liu","affiliation":"School of Computer and Engineering , Nanjing University of Science and Technology"},{"name":"Yew-Soon Ong","slug":"yew-soon-ong","given":"Yew-Soon","family":"Ong","affiliation":"‡ School of Computer Science and Engineering , Nanyang Technological University Centre for Artificial Intelligence , FEIT , University of Technology Sydney"},{"name":"Quan-Sen Sun","slug":"quan-sen-sun","given":"Quan-Sen","family":"Sun","affiliation":"§ School of Computer Science and Engineering , The University of New South Wales"}],"abstract":"Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in continuous vector, which imposes formidable challenges in storage and computation costs, particularly in large-scale applications. To address the issue, this paper proposes a novel discrete network embedding (DNE) for more compact representations. In particular, DNE learns short binary codes to represent each node. The Hamming similarity between two binary embeddings is then employed to well approximate the ground-truth similarity. A novel discrete multi-class classifier is also developed to expedite classification. Moreover, we propose to jointly learn the discrete embedding and classifier within a unified framework to improve the compactness and discrimination of network embedding. Extensive experiments on node classification consistently demonstrate that DNE exhibits lower storage and computational complexity than state-of-the-art network embedding methods, while obtains competitive classification results.","doi":"10.24963/ijcai.2018/493","url":"https://ijcai.org/proceedings/2018/493","pdf":"https://ijcai.org/proceedings/2018/0493.pdf","markdown":"https://ijcai.org/proceedings/2018/493.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/493","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":493,"pages":{"first":3549,"last":3555},"keywords":["Machine Learning: Data Mining","Machine Learning: Relational Learning","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2016] Shaosheng Cao, Wei Lu, and Qiongkai X- u. 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