Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80424
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dc.contributor.authorLong, YFen_US
dc.contributor.authorXiang, Ren_US
dc.contributor.authorLu, Qen_US
dc.contributor.authorXiong, Den_US
dc.contributor.authorHuang, CRen_US
dc.contributor.authorBi, CLen_US
dc.contributor.authorLi, MLen_US
dc.date.accessioned2019-03-26T09:17:05Z-
dc.date.available2019-03-26T09:17:05Z-
dc.date.issued2018-
dc.identifier.citationIEEE access, 2018, v. 6, p. 55850-55860en_US
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10397/80424-
dc.description.abstractFinding methods to represent multiple types of nodes in heterogeneous networks is both challenging and rewarding, as there is much less work in this area compared with that of homogeneous networks. In this paper, we propose a novel approach to learn node embedding for heterogeneous networks through a joint learning framework of both network links and text associated with nodes. A novel attention mechanism is also used to make good use of text extended through links to obtain much larger network context. Link embedding is first learned through a random-walk-based method to process multiple types of links. Text embedding is separately learned at both sentence level and document level to capture salient semantic information more comprehensively. Then, both types of embeddings are jointly fed into a hierarchical neural network model to learn node representation through mutual enhancement. The attention mechanism follows linked edges to obtain context of adjacent nodes to extend context for node representation. The evaluation on a link prediction task in a heterogeneous network data set shows that our method outperforms the current state-of-the-art method by 2.5%-5.0% in AUC values with p-value less than 10(-9), indicating very significant improvement.en_US
dc.description.sponsorshipDepartment of Chinese and Bilingual Studiesen_US
dc.description.sponsorshipDepartment of Computingen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE accessen_US
dc.rights© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.en_US
dc.rightsPost with permission of the publisher.en_US
dc.rightsThe following publication Long, Y.F., Xiang, R., Lu, Q., Xiong, D., Huang, C.R., Bi, C.L., & Li, M.L. (2018). Learning heterogeneous network embedding from text and links. IEEE Access, 6, 55850-55860 is available at https://dx.doi.org/10.1109/ACCESS.2018.2873044en_US
dc.subjectNetwork embeddingen_US
dc.subjectHeterogeneous networken_US
dc.subjectAttention mechanismen_US
dc.subjectText processingen_US
dc.titleLearning heterogeneous network embedding from text and linksen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage55850-
dc.identifier.epage55860-
dc.identifier.volume6-
dc.identifier.doi10.1109/ACCESS.2018.2873044-
dc.identifier.isiWOS:000448288800001-
dc.description.validate201903 bcrc-
dc.description.oapublished_final-
Appears in Collections:Journal/Magazine Article
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