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dc.contributor.authorChen, Yujun
dc.contributor.authorSun, Ke
dc.contributor.authorPu, Juhua
dc.contributor.authorXiong, Zhang
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2019-11-04T13:41:00Z
dc.date.available2019-11-04T13:41:00Z
dc.date.issued2019-10-25
dc.identifier.citationChen, Y., Sun, K., Pu, J., Xiong, Z., & Zhang, X. (2019). GraPASA: Parametric Graph Embedding via Siamese Architecture. Information Sciences. doi:10.1016/j.ins.2019.10.027
dc.identifier.doi10.1016/j.ins.2019.10.027
dc.identifier.urihttp://hdl.handle.net/10754/659514
dc.description.abstractGraph representation learning or graph embedding is a classical topic in data mining. Current embedding methods are mostly non-parametric, where all the embedding points are unconstrained free points in the target space. These approaches suffer from limited scalability and an over-flexible representation. In this paper, we propose a parametric graph embedding by fusing graph topology information and node content information. The embedding points are obtained through a highly flexible non-linear transformation from node content features to the target space. This transformation is learned using the contrastive loss function of the siamese network to preserve node adjacency in the input graph. On several benchmark network datasets, the proposed GraPASA method shows a significant margin over state-of-the-art techniques on benchmark graph representation tasks.
dc.description.sponsorshipThis work was partially supported and funded by King Abdullah University of Science and Technology (KAUST) through the KAUST Office of Sponsored Research (OSR) under Award No. 2639, National Key R&D Program of China (2017YFC0803700), National Natural Science Foundation of China (61502320), Science Foundation of Shenzhen City in China (JCYJ20160419152942010), the State Key Laboratory of Software Development Environment, and Aeronautical Science Foundation of China.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0020025519309806
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, [[Volume], [Issue], (2019-10-25)] DOI: 10.1016/j.ins.2019.10.027 . © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNetwork embedding
dc.subjectInductive representation learning
dc.subjectSiamese network
dc.subjectinformation fusion
dc.titleGraPASA: Parametric Graph Embedding via Siamese Architecture
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalInformation Sciences
dc.rights.embargodate2021-10-25
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Engineering, Beihang University, No.37 Xueyuan Road, Beijing, 100191, China
dc.contributor.institutionCSIRO’s Data61, Australia
kaust.personZhang, Xiangliang
kaust.grant.numberAward No. 2639
refterms.dateFOA2019-11-04T13:41:41Z
kaust.acknowledged.supportUnitKAUST Office of Sponsored Research (OSR)
dc.date.published-online2019-10-25
dc.date.published-print2019-10


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