Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder

Social network alignment, aligning different social networks on their common users, is receiving increasing attention from both academic and industry. Most of the existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks con...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on knowledge and data engineering Vol. 35; no. 6; pp. 5597 - 5611
Main Authors:	Sun, Li, Zhang, Zhongbao, Wang, Feiyang, Ji, Pengxin, Wen, Jian, Su, Sen, Yu, Philip S.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Alignment Convergence Decoding Design optimization Dynamics graph neural networks Heuristic algorithms network alignment network embedding Optimization Social networking (online) Social networks Solids Tensors
ISSN:	1041-4347, 1558-2191
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Social network alignment, aligning different social networks on their common users, is receiving increasing attention from both academic and industry. Most of the existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA , unfolding the fruitful dynamics of social networks for user alignment. However, it faces challenges in both modeling and optimization: (1) To model the intra-network dynamics, we design a novel dynamic graph autoencoder to learn user embeddings with complex network dynamics. (2) To model the inter-network alignment, we design a unified optimization framework over proposed dynamic graph autoencoders, constructing a common subspace for user alignment across different networks. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed approach substantially outperforms the state-of-the-art methods.
AbstractList	Social network alignment, aligning different social networks on their common users, is receiving increasing attention from both academic and industry. Most of the existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA , unfolding the fruitful dynamics of social networks for user alignment. However, it faces challenges in both modeling and optimization: (1) To model the intra-network dynamics, we design a novel dynamic graph autoencoder to learn user embeddings with complex network dynamics. (2) To model the inter-network alignment, we design a unified optimization framework over proposed dynamic graph autoencoders, constructing a common subspace for user alignment across different networks. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed approach substantially outperforms the state-of-the-art methods.
Author	Wen, Jian Ji, Pengxin Sun, Li Zhang, Zhongbao Yu, Philip S. Wang, Feiyang Su, Sen
Author_xml	– sequence: 1 givenname: Li orcidid: 0000-0003-4562-2279 surname: Sun fullname: Sun, Li email: ccesunli@ncepu.edu.cn organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 2 givenname: Zhongbao orcidid: 0000-0002-3242-150X surname: Zhang fullname: Zhang, Zhongbao email: zhongbaozb@bupt.edu.cn organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 3 givenname: Feiyang surname: Wang fullname: Wang, Feiyang email: fywang@bupt.edu.cn organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 4 givenname: Pengxin surname: Ji fullname: Ji, Pengxin email: jipx@bupt.edu.cn organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 5 givenname: Jian surname: Wen fullname: Wen, Jian email: j.wen@bupt.edu.cn organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 6 givenname: Sen orcidid: 0000-0003-4266-7527 surname: Su fullname: Su, Sen email: susen@bupt.edu.cn organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 7 givenname: Philip S. orcidid: 0000-0002-3491-5968 surname: Yu fullname: Yu, Philip S. email: psyu@uic.edu organization: Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
BookMark	eNp9kE9PwjAYxhuDiYB-AONliefNtlvX1tsCiEYiJuK5KaXD4mhnVzT46R1COHjw9P7J83ufvE8PdKyzGoBLBBOEIL-ZPQ5HCYYYJykimEB8ArqIEBZjxFGn7WGG4izN6BnoNc0KQsgoQ13wXFRmaY1dRsOtlWujohenjKyiJx2-nH9vbqPCRtM6mLX5lsG4dvjU_qgee1m_RcUmOG2VW2h_Dk5LWTX64lD74PVuNBvcx5Pp-GFQTGKFeRpilueKS004XchFmhOteCZTijKiYcYkLMscqVKyOYEMq6xtcclpzmm7KudEpX1wvb9be_ex0U0QK7fxtrUUmEHGMKOYtSq0VynvmsbrUtTerKXfCgTFLjixC07sghOH4FqG_mGUCb-vBy9N9S95tSeN1vroxCnKGYLpDxJYfPQ
CODEN	ITKEEH
CitedBy_id	crossref_primary_10_1109_TKDE_2024_3447123 crossref_primary_10_1109_TFUZZ_2025_3562944 crossref_primary_10_1016_j_comnet_2025_111607 crossref_primary_10_1109_TBDATA_2024_3407543 crossref_primary_10_1007_s13042_024_02384_0 crossref_primary_10_1145_3580509 crossref_primary_10_1016_j_hcc_2024_100293 crossref_primary_10_1109_TKDE_2023_3312358 crossref_primary_10_1109_TKDE_2025_3566064
Cites_doi	10.1145/3219819.3220068 10.1145/3308558.3313562 10.1109/BigData47090.2019.9006430 10.1109/INFOCOM.2019.8737542 10.1145/2505515.2505531 10.1007/978-3-319-46128-1_29 10.1109/ICDM.2018.00182 10.1109/TKDE.2018.2849727 10.1145/3289600.3290989 10.1109/TPAMI.2008.277 10.1109/INFOCOM.2018.8486231 10.1145/2487575.2487648 10.1609/aaai.v28i1.8720 10.1145/3068777.3068781 10.1145/2588555.2588559 10.24963/ijcai.2018/537 10.1109/ICDM.2015.114 10.1145/1401890.1402008 10.1145/2939672.2939849 10.1145/3018661.3018667 10.1609/aaai.v33i01.3301996 10.1145/3219819.3220054 10.1093/bioinformatics/btz119 10.1609/icwsm.v3i1.13993 10.1145/3308558.3313484 10.1145/3219819.3220024 10.1089/cyber.2010.0651 10.1145/2939672.2939754 10.1609/aaai.v32i1.11257 10.1145/3219819.3220034 10.1109/ICDE.2019.00174 10.1145/2783258.2783268 10.1145/2939672.2939766 10.24963/ijcai.2018/288 10.1609/aaai.v32i1.12014 10.1007/s11280-017-0490-9 10.1145/3269206.3271675 10.1145/3269206.3271705 10.1109/TBDATA.2018.2850013 10.1145/2736277.2741093 10.1089/cmb.2009.0099 10.24963/ijcai.2018/362 10.1145/2623330.2623732 10.1145/3308558.3313430
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
DOI	10.1109/TKDE.2022.3152502
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1558-2191
EndPage	5611
ExternalDocumentID	10_1109_TKDE_2022_3152502 9716810
Genre	orig-research
GrantInformation_xml	– fundername: National Key Research and Development Program of China grantid: 2018YFB1003804 – fundername: NSFC grantid: U1936103; 61921003 – fundername: National Science Foundation grantid: III-1763325; III-1909323; III-2106758; SaTC-1930941 funderid: 10.13039/501100008982
GroupedDBID	-~X .DC 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB 1OL 5VS 9M8 AAYXX ABFSI AETIX AGSQL AI. AIBXA ALLEH CITATION E.L H~9 ICLAB IFJZH RNI RZB TAF VH1 7SC 7SP 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c293t-866c9ae597dad365ec94a37145e048a0ff61cfa8b5082c4cfa2f97697a8bfb5c3
IEDL.DBID	RIE
ISICitedReferencesCount	15
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000981944600011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1041-4347
IngestDate	Sun Jun 29 15:26:13 EDT 2025 Tue Nov 18 22:11:36 EST 2025 Sat Nov 29 02:36:05 EST 2025 Wed Aug 27 02:18:12 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c293t-866c9ae597dad365ec94a37145e048a0ff61cfa8b5082c4cfa2f97697a8bfb5c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-4562-2279 0000-0003-4266-7527 0000-0002-3491-5968 0000-0002-3242-150X
PQID	2808828728
PQPubID	85438
PageCount	15
ParticipantIDs	crossref_primary_10_1109_TKDE_2022_3152502 ieee_primary_9716810 proquest_journals_2808828728 crossref_citationtrail_10_1109_TKDE_2022_3152502
PublicationCentury	2000
PublicationDate	2023-06-01
PublicationDateYYYYMMDD	2023-06-01
PublicationDate_xml	– month: 06 year: 2023 text: 2023-06-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on knowledge and data engineering
PublicationTitleAbbrev	TKDE
PublicationYear	2023
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref15 ref14 ref11 ref10 liu (ref4) 2016 graves (ref23) 2014 ref17 ref16 ref19 ref18 lee (ref25) 2001 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref43 ref49 kipf (ref20) 2017 ref8 velickovic (ref21) 2018 ref7 ref9 ref3 ref6 ref40 ref35 ref34 ref37 ref36 man (ref5) 2016 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref26 ref22 ref28 ref27 ref29 yang (ref44) 2015
References_xml	– ident: ref41 doi: 10.1145/3219819.3220068 – ident: ref22 doi: 10.1145/3308558.3313562 – ident: ref29 doi: 10.1109/BigData47090.2019.9006430 – ident: ref16 doi: 10.1109/INFOCOM.2019.8737542 – start-page: 556 year: 2001 ident: ref25 article-title: Algorithms for non-negative matrix factorization publication-title: Proc 13th Int Conf Neural Inf Process Syst – ident: ref1 doi: 10.1145/2505515.2505531 – ident: ref10 doi: 10.1007/978-3-319-46128-1_29 – ident: ref15 doi: 10.1109/ICDM.2018.00182 – ident: ref50 doi: 10.1109/TKDE.2018.2849727 – ident: ref24 doi: 10.1145/3289600.3290989 – ident: ref26 doi: 10.1109/TPAMI.2008.277 – ident: ref6 doi: 10.1109/INFOCOM.2018.8486231 – ident: ref2 doi: 10.1145/2487575.2487648 – ident: ref3 doi: 10.1609/aaai.v28i1.8720 – ident: ref35 doi: 10.1145/3068777.3068781 – ident: ref31 doi: 10.1145/2588555.2588559 – ident: ref11 doi: 10.24963/ijcai.2018/537 – ident: ref9 doi: 10.1109/ICDM.2015.114 – start-page: 1774 year: 2016 ident: ref4 article-title: Aligning users across social networks using network embedding publication-title: Proc 25th Int Joint Conf Artif Intell – ident: ref27 doi: 10.1145/1401890.1402008 – ident: ref19 doi: 10.1145/2939672.2939849 – ident: ref45 doi: 10.1145/3018661.3018667 – ident: ref33 doi: 10.1609/aaai.v33i01.3301996 – ident: ref48 doi: 10.1145/3219819.3220054 – ident: ref37 doi: 10.1093/bioinformatics/btz119 – ident: ref30 doi: 10.1609/icwsm.v3i1.13993 – ident: ref28 doi: 10.1145/3308558.3313484 – ident: ref49 doi: 10.1145/3219819.3220024 – ident: ref18 doi: 10.1089/cyber.2010.0651 – start-page: 2111 year: 2015 ident: ref44 article-title: Network representation learning with rich text information publication-title: Proc 24th Int Conf Artif Intell – ident: ref39 doi: 10.1145/2939672.2939754 – ident: ref47 doi: 10.1609/aaai.v32i1.11257 – ident: ref43 doi: 10.1145/3219819.3220034 – ident: ref32 doi: 10.1109/ICDE.2019.00174 – start-page: 1 year: 2018 ident: ref21 article-title: Graph attention networks publication-title: Proc 6th Int Conf Learn Representations – ident: ref8 doi: 10.1145/2783258.2783268 – ident: ref34 doi: 10.1145/2939672.2939766 – start-page: 1764 year: 2014 ident: ref23 article-title: Towards end-to-end speech recognition with recurrent neural networks publication-title: Proc 31st Int Conf Int Conf Mach Learn – ident: ref46 doi: 10.24963/ijcai.2018/288 – ident: ref12 doi: 10.1609/aaai.v32i1.12014 – ident: ref14 doi: 10.1007/s11280-017-0490-9 – ident: ref7 doi: 10.1145/3269206.3271675 – ident: ref13 doi: 10.1145/3269206.3271705 – ident: ref51 doi: 10.1109/TBDATA.2018.2850013 – start-page: 1823 year: 2016 ident: ref5 article-title: Predict anchor links across social networks via an embedding approach publication-title: Proc 25th Int Joint Conf Artif Intell – ident: ref40 doi: 10.1145/2736277.2741093 – ident: ref36 doi: 10.1089/cmb.2009.0099 – ident: ref42 doi: 10.24963/ijcai.2018/362 – ident: ref38 doi: 10.1145/2623330.2623732 – start-page: 1 year: 2017 ident: ref20 article-title: Semi-supervised classification with graph convolutional networks publication-title: Proc 5th Int Conf Learn Representations – ident: ref17 doi: 10.1145/3308558.3313430
SSID	ssj0008781
Score	2.5111117
Snippet	Social network alignment, aligning different social networks on their common users, is receiving increasing attention from both academic and industry. Most of...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	5597
SubjectTerms	Algorithms Alignment Convergence Decoding Design optimization Dynamics graph neural networks Heuristic algorithms network alignment network embedding Optimization Social networking (online) Social networks Solids Tensors
Title	Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder
URI	https://ieeexplore.ieee.org/document/9716810 https://www.proquest.com/docview/2808828728
Volume	35
WOSCitedRecordID	wos000981944600011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2191 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0008781 issn: 1041-4347 databaseCode: RIE dateStart: 19890101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFH6oeNCD001x_iIHT2J1S9cm8TbcVFC2HabsVtI0lcHcZOv8-32vzYqiCN5KSaD0S17el-R9H8C5xixCxcZ6idah10KG4iktrcdlyi2Pm1bmoj4vT6LXk6ORGqzBZVkLY63NL5_ZK3rMz_KTmVnSVtk1yR1JqqdaF0IUtVpl1JUiNyRFdoGcyG8Jd4LZbKjr4WOni0yQcySodIrHv61BuanKj0icLy93lf992C7suDSStQvc92DNTqtQWVk0MDdjq7D9RW-wBoP2ZPxKGyGsUxjRs6I6l_WKy-CLG9aesj5GkTdXnsn6ONTL1vekbs3ay2xG8peJne_D8113ePvgOUsFz-C6nnkyDI3SFllEohM_DKxRLU2ifYHFqawbaRo2TapljHkbNy185CkmLErgqzQOjH8AG9PZ1B4Ci3mgEysSESPj0n5AgVL6BsHnioTE6tBY_eTIOL1xsr2YRDnvaKiIcIkIl8jhUoeLsst7IbbxV-MaAVE2dBjU4WSFZOSm4yLikpiEFFwe_d7rGLbIR764A3YCG9l8aU9h03xk48X8LB9pnx7b0F8
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5KFdSDj6pYrZqDJ3Ftm30l3opvrLWHKr0t2WxWCrWVPvz9zuymi6II3pYlgWW_ZDJfkvk-gBOFWYSMtXESpQLHQ4biSCWMw0XKDY-bRmSiPi_tsNMR_b7sluCsqIUxxmSXz8w5PWZn-clYz2mrrE5yR4LqqZZ8z-PNvFqriLsizCxJkV8gK3K90J5hNhuy3nu4ukYuyDlSVDrH499WocxW5UcszhaYm43_fdomrNtEkrVy5LegZEYV2FiYNDA7Zyuw9kVxcBu6reHglbZC2FVuRc_y-lzWya-DTy9Ya8SeMI682QJN9oSDvWh9S_rWrDWfjUkAMzGTHXi-ue5d3jnWVMHRuLLPHBEEWiqDPCJRiRv4RktPkWyfb3Ayq0aaBk2dKhFj5sa1h488xZRFhvgqjX3t7kJ5NB6ZPWAx91ViwiSMkXMp16dQKVyN8HNJUmJVaCx-cqSt4jgZXwyjjHk0ZES4RIRLZHGpwmnR5T2X2_ir8TYBUTS0GFShtkAyshNyGnFBXEKEXOz_3usYVu56j-2ofd95OIBVcpXPb4TVoDybzM0hLOuP2WA6OcpG3ScfAtOm
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Aligning+Dynamic+Social+Networks%3A+An+Optimization+Over+Dynamic+Graph+Autoencoder&rft.jtitle=IEEE+transactions+on+knowledge+and+data+engineering&rft.au=Sun%2C+Li&rft.au=Zhang%2C+Zhongbao&rft.au=Wang%2C+Feiyang&rft.au=Ji%2C+Pengxin&rft.date=2023-06-01&rft.pub=IEEE&rft.issn=1041-4347&rft.volume=35&rft.issue=6&rft.spage=5597&rft.epage=5611&rft_id=info:doi/10.1109%2FTKDE.2022.3152502&rft.externalDocID=9716810
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1041-4347&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1041-4347&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1041-4347&client=summon