Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105494
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Xu, L | en_US |
dc.creator | Cao, J | en_US |
dc.creator | Wei, X | en_US |
dc.creator | Yu, PS | en_US |
dc.date.accessioned | 2024-04-15T07:34:41Z | - |
dc.date.available | 2024-04-15T07:34:41Z | - |
dc.identifier.issn | 1041-4347 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105494 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication L. Xu, J. Cao, X. Wei and P. S. Yu, "Network Embedding via Coupled Kernelized Multi-Dimensional Array Factorization," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 12, pp. 2414-2425, 1 Dec. 2020 is available at https://doi.org/10.1109/TKDE.2019.2931833. | en_US |
dc.subject | Kernelized array factorization | en_US |
dc.subject | Link prediction | en_US |
dc.subject | Multi-label classification | en_US |
dc.subject | Network embedding | en_US |
dc.title | Network embedding via coupled kernelized multi-dimensional array factorization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2414 | en_US |
dc.identifier.epage | 2425 | en_US |
dc.identifier.volume | 32 | en_US |
dc.identifier.issue | 12 | en_US |
dc.identifier.doi | 10.1109/TKDE.2019.2931833 | en_US |
dcterms.abstract | Network embedding has been widely employed in networked data mining applications as it can learn low-dimensional and dense node representations from the high-dimensional and sparse network structure. While most existing network embedding methods only model the proximity between two nodes regardless of the order of the proximity, this paper proposes to explicitly model multi-node proximities which can be widely observed in practice, e.g., multiple researchers coauthor a paper, and multiple genes co-express a protein. Explicitly modeling multi-node proximities is important because some two-node interactions may not come into existence without a third node. By proving that LINE(1st), a recent network embedding method, is equivalent to kernelized matrix factorization, this paper proposes coupled kernelized multi-dimensional array factorization (Cetera) which jointly factorizes multiple multi-dimensional arrays by enforcing a consensus representation for each node. In this way, node representations can be more comprehensive and effective, which is demonstrated on three real-world networks through link prediction and multi-label classification. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on knowledge and data engineering, 1 Dec. 2020, v. 32, no. 12, p. 2414-2425 | en_US |
dcterms.isPartOf | IEEE transactions on knowledge and data engineering | en_US |
dcterms.issued | 2020-12-01 | - |
dc.identifier.scopus | 2-s2.0-85096175343 | - |
dc.identifier.eissn | 1558-2191 | en_US |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0161 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Key R&D Program of China; NSF | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 43657943 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xu_Network_Embedding_Via.pdf | Pre-Published version | 2.32 MB | Adobe PDF | View/Open |
Page views
28
Citations as of Jul 7, 2024
Downloads
17
Citations as of Jul 7, 2024
SCOPUSTM
Citations
5
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
5
Citations as of Jul 4, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.