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dc.contributor.authorMeng, Zaiqiao
dc.contributor.authorLiang, Shangsong
dc.contributor.authorBao, Hongyan
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2019-03-28T07:35:34Z
dc.date.available2019-03-28T07:35:34Z
dc.date.issued2019-03-11
dc.identifier.citationMeng Z, Liang S, Bao H, Zhang X (2019) Co-Embedding Attributed Networks. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM ’19. Available: http://dx.doi.org/10.1145/3289600.3291015.
dc.identifier.doi10.1145/3289600.3291015
dc.identifier.urihttp://hdl.handle.net/10754/631764
dc.description.abstractExisting embedding methods for attributed networks aim at learning low-dimensional vector representations for nodes only but not for both nodes and attributes, resulting in the fact that they cannot capture the affinities between nodes and attributes. However, capturing such affinities is of great importance to the success of many real-world attributed network applications, such as attribute inference and user profiling. Accordingly, in this paper, we introduce a Co-embedding model for Attributed Networks (CAN), which learns low-dimensional representations of both attributes and nodes in the same semantic space such that the affinities between them can be effectively captured and measured. To obtain high-quality embeddings, we propose a variational auto-encoder that embeds each node and attribute with means and variances of Gaussian distributions. Experimental results on real-world networks demonstrate that our model yields excellent performance in a number of applications compared with state-of-the-art techniques.
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-19-01.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3289600.3291015
dc.rightsArchived with thanks to Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM '19
dc.subjectAttributed network
dc.subjectNetwork embedding
dc.subjectVariational auto-encoder
dc.titleCo-Embedding Attributed Networks
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalProceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM '19
dc.conference.date2019-02-11 to 2019-02-15
dc.conference.name12th ACM International Conference on Web Search and Data Mining, WSDM 2019
dc.conference.locationMelbourne, VIC, AUS
dc.eprint.versionPost-print
kaust.personMeng, Zaiqiao
kaust.personLiang, Shangsong
kaust.personBao, Hongyan
kaust.personZhang, Xiangliang
kaust.grant.numberFCC/1/1976-19-01
refterms.dateFOA2019-03-28T07:37:12Z
dc.date.published-online2019-03-11
dc.date.published-print2019


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