{"id":"https://openalex.org/W4292263718","doi":"https://doi.org/10.1109/tsmc.2022.3196506","title":"Dynamic Representation Learning via Recurrent Graph Neural Networks","display_name":"Dynamic Representation Learning via Recurrent Graph Neural Networks","publication_year":2022,"publication_date":"2022-08-17","ids":{"openalex":"https://openalex.org/W4292263718","doi":"https://doi.org/10.1109/tsmc.2022.3196506"},"language":"en","primary_location":{"id":"doi:10.1109/tsmc.2022.3196506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2022.3196506","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085340418","display_name":"Chun-Yang Zhang","orcid":"https://orcid.org/0000-0001-6151-7028"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun-Yang Zhang","raw_affiliation_strings":["School of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6151-7028","affiliations":[{"raw_affiliation_string":"School of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074115209","display_name":"Zhi-Liang Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi-Liang Yao","raw_affiliation_strings":["School of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011073406","display_name":"Hong-Yu Yao","orcid":"https://orcid.org/0000-0001-8126-0733"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong-Yu Yao","raw_affiliation_strings":["School of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8126-0733","affiliations":[{"raw_affiliation_string":"School of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047781906","display_name":"Feng Huang","orcid":"https://orcid.org/0000-0003-4652-4312"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Huang","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4652-4312","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100643265","display_name":"C. L. Philip Chen","orcid":"https://orcid.org/0000-0001-5451-7230"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"C. L. Philip Chen","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5451-7230","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.5945,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.93595321,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"53","issue":"2","first_page":"1284","last_page":"1297"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6888182759284973},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6226421594619751},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5617058277130127},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5603113770484924},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.554793655872345},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5194429755210876},{"id":"https://openalex.org/keywords/topological-graph-theory","display_name":"Topological graph theory","score":0.4991436004638672},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4456503987312317},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42881307005882263},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3959874212741852},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.3717498481273651},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17219087481498718},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.14511814713478088},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.12486922740936279}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6888182759284973},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6226421594619751},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5617058277130127},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5603113770484924},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.554793655872345},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5194429755210876},{"id":"https://openalex.org/C157406716","wikidata":"https://www.wikidata.org/wiki/Q4115842","display_name":"Topological graph theory","level":5,"score":0.4991436004638672},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4456503987312317},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42881307005882263},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3959874212741852},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.3717498481273651},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17219087481498718},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.14511814713478088},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.12486922740936279},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsmc.2022.3196506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2022.3196506","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3271887843","display_name":null,"funder_award_id":"62076065","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4512728348","display_name":null,"funder_award_id":"2020J01495","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":84,"referenced_works":["https://openalex.org/W348384746","https://openalex.org/W1522301498","https://openalex.org/W1888005072","https://openalex.org/W2053186076","https://openalex.org/W2064675550","https://openalex.org/W2088412871","https://openalex.org/W2111708605","https://openalex.org/W2138399168","https://openalex.org/W2142535891","https://openalex.org/W2154851992","https://openalex.org/W2156718197","https://openalex.org/W2157331557","https://openalex.org/W2187089797","https://openalex.org/W2271840356","https://openalex.org/W2387462954","https://openalex.org/W2393319904","https://openalex.org/W2530863813","https://openalex.org/W2565330852","https://openalex.org/W2585835859","https://openalex.org/W2604366058","https://openalex.org/W2604942799","https://openalex.org/W2604983939","https://openalex.org/W2605234117","https://openalex.org/W2610034660","https://openalex.org/W2612872092","https://openalex.org/W2623187518","https://openalex.org/W2700550412","https://openalex.org/W2781951010","https://openalex.org/W2783466287","https://openalex.org/W2790197930","https://openalex.org/W2795735740","https://openalex.org/W2798918712","https://openalex.org/W2806983170","https://openalex.org/W2808087697","https://openalex.org/W2808771744","https://openalex.org/W2808908091","https://openalex.org/W2901504064","https://openalex.org/W2903329593","https://openalex.org/W2913932916","https://openalex.org/W2935726879","https://openalex.org/W2950898568","https://openalex.org/W2951008357","https://openalex.org/W2954691982","https://openalex.org/W2962756421","https://openalex.org/W2963169753","https://openalex.org/W2963224980","https://openalex.org/W2963312446","https://openalex.org/W2963512530","https://openalex.org/W2963603080","https://openalex.org/W2963920355","https://openalex.org/W2964015378","https://openalex.org/W2986197715","https://openalex.org/W2997574889","https://openalex.org/W2998313947","https://openalex.org/W2999301998","https://openalex.org/W3026076535","https://openalex.org/W3100330855","https://openalex.org/W3103254545","https://openalex.org/W3104097132","https://openalex.org/W4232932184","https://openalex.org/W4246941204","https://openalex.org/W4293651439","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4303633609","https://openalex.org/W4394669517","https://openalex.org/W6611947346","https://openalex.org/W6631190155","https://openalex.org/W6638318767","https://openalex.org/W6681096077","https://openalex.org/W6682755970","https://openalex.org/W6684821475","https://openalex.org/W6690815549","https://openalex.org/W6694517276","https://openalex.org/W6699364125","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6744557953","https://openalex.org/W6748660260","https://openalex.org/W6751747363","https://openalex.org/W6758327135","https://openalex.org/W6761150088","https://openalex.org/W6776137863","https://openalex.org/W6864429413"],"related_works":["https://openalex.org/W3008584592","https://openalex.org/W3048601286","https://openalex.org/W2965925734","https://openalex.org/W4285218279","https://openalex.org/W4308164949","https://openalex.org/W2995533131","https://openalex.org/W4312576172","https://openalex.org/W4205832324","https://openalex.org/W4386136067","https://openalex.org/W3035534438"],"abstract_inverted_index":{"A":[0],"large":[1],"number":[2],"of":[3,70,87,126,172],"real-world":[4,202],"systems":[5],"generate":[6],"graphs":[7,121],"that":[8,151],"are":[9,18,68],"structured":[10],"data":[11],"aligned":[12],"with":[13],"nodes":[14,25,91],"and":[15,49,59,76,174,201],"edges.":[16],"Graphs":[17],"usually":[19],"dynamic":[20,66,141],"in":[21,41,164,210],"many":[22],"scenarios,":[23],"where":[24],"or":[26,52,90],"edges":[27],"keep":[28],"evolving":[29,192],"over":[30],"time.":[31],"Recently,":[32],"graph":[33,89,95,127,157],"representation":[34,73,137],"learning":[35,74,124,138],"(GRL)":[36],"has":[37],"received":[38],"great":[39],"success":[40],"network":[42,60,80,159],"analysis,":[43],"which":[44],"aims":[45],"to":[46,160,181,188,214],"produce":[47,161],"informative":[48],"representative":[50],"features":[51],"low-dimensional":[53],"embeddings":[54],"by":[55],"exploring":[56],"node":[57],"attributes":[58],"topology.":[61],"Most":[62],"state-of-the-art":[63,216],"models":[64],"for":[65,140],"GRL":[67],"composed":[69],"a":[71,77,88,97,136,148,156,165],"static":[72,94],"model":[75,139,150,187],"recurrent":[78],"neural":[79,158],"(RNN).":[81],"The":[82],"former":[83],"generates":[84],"the":[85,102,105,112,116,123,170,186,195,204,215],"representations":[86,163],"from":[92,179],"one":[93],"at":[96],"discrete":[98],"time":[99],"step,":[100],"while":[101],"latter":[103],"captures":[104],"temporal":[106,117,173],"correlation":[107],"between":[108,119],"adjacent":[109],"graphs.":[110],"However,":[111],"two-stage":[113],"design":[114],"ignores":[115],"dynamics":[118],"contiguous":[120],"during":[122],"processing":[125],"representations.":[128],"To":[129],"alleviate":[130],"this":[131,133],"problem,":[132],"article":[134],"proposes":[135],"graphs,":[142],"called":[143],"DynGNN.":[144],"Differently,":[145],"it":[146],"is":[147],"single-stage":[149],"embeds":[152],"an":[153],"RNN":[154],"into":[155,177],"better":[162],"compact":[166],"form.":[167],"This":[168],"takes":[169],"fusion":[171],"topology":[175],"correlations":[176],"account":[178],"low-level":[180],"high-level":[182],"feature":[183],"learning,":[184],"enabling":[185],"capture":[189],"more":[190],"fine-grained":[191],"patterns.":[193],"From":[194],"experimental":[196],"results":[197],"on":[198],"both":[199],"synthetic":[200],"networks,":[203],"proposed":[205],"DynGNN":[206],"yields":[207],"significant":[208],"improvements":[209],"multiple":[211],"tasks":[212],"compared":[213],"counterparts.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":5}],"updated_date":"2026-08-12T07:12:00.856984","created_date":"2025-10-10T00:00:00"}
