{"id":"https://openalex.org/W4407097816","doi":"https://doi.org/10.1109/tnnls.2025.3526944","title":"Boosting Temporal Graph Learning From Perspectives of Global and Local Structures","display_name":"Boosting Temporal Graph Learning From Perspectives of Global and Local Structures","publication_year":2025,"publication_date":"2025-02-03","ids":{"openalex":"https://openalex.org/W4407097816","doi":"https://doi.org/10.1109/tnnls.2025.3526944","pmid":"https://pubmed.ncbi.nlm.nih.gov/40031389"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3526944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3526944","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5077487314","display_name":"Fengyi Wang","orcid":"https://orcid.org/0009-0009-5445-954X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengyi Wang","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0009-5445-954X","affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086985082","display_name":"Guanghui Zhu","orcid":"https://orcid.org/0000-0002-5069-5950"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanghui Zhu","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-5069-5950","affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076450877","display_name":"Hongqing Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongqing Ding","raw_affiliation_strings":["Department of Planning and Construction, China Mobile Communications Group Company Ltd., Beijing, China","Department of Planning and Construction, China Mobile Communications Group Company Ltd, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Planning and Construction, China Mobile Communications Group Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Department of Planning and Construction, China Mobile Communications Group Company Ltd, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100426115","display_name":"Pengfei Zhang","orcid":"https://orcid.org/0000-0002-9458-5067"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Zhang","raw_affiliation_strings":["Department of Planning and Construction, China Mobile Communications Group Company Ltd., Beijing, China","Department of Planning and Construction, China Mobile Communications Group Company Ltd, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Planning and Construction, China Mobile Communications Group Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Department of Planning and Construction, China Mobile Communications Group Company Ltd, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115598059","display_name":"Chunfeng Yuan","orcid":"https://orcid.org/0000-0002-8746-8137"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunfeng Yuan","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8746-8137","affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007538828","display_name":"Yihua Huang","orcid":"https://orcid.org/0000-0003-1806-0936"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihua Huang","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-1806-0936","affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0534,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.92379456,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"36","issue":"8","first_page":"15359","last_page":"15373"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9128000140190125,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9128000140190125,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/boosting","display_name":"Boosting (machine learning)","score":0.7796980142593384},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5193087458610535},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.500645637512207},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43350857496261597},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.34095239639282227},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3319924473762512},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.30733832716941833}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7796980142593384},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5193087458610535},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.500645637512207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43350857496261597},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34095239639282227},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3319924473762512},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30733832716941833}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3526944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3526944","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:40031389","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40031389","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6824831872","display_name":null,"funder_award_id":"62102177","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320317102","display_name":"National Center of Science and Technology Evaluation","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":88,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W1979104937","https://openalex.org/W2090891622","https://openalex.org/W2107878631","https://openalex.org/W2123948326","https://openalex.org/W2143545157","https://openalex.org/W2154851992","https://openalex.org/W2387462954","https://openalex.org/W2393319904","https://openalex.org/W2405761224","https://openalex.org/W2562676961","https://openalex.org/W2565330852","https://openalex.org/W2610034660","https://openalex.org/W2767597557","https://openalex.org/W2769816764","https://openalex.org/W2787927827","https://openalex.org/W2808087697","https://openalex.org/W2808409763","https://openalex.org/W2901504064","https://openalex.org/W2904736919","https://openalex.org/W2907492528","https://openalex.org/W2945827377","https://openalex.org/W2962756421","https://openalex.org/W2963601856","https://openalex.org/W2964015378","https://openalex.org/W2965683718","https://openalex.org/W2969996838","https://openalex.org/W2986068433","https://openalex.org/W2995590401","https://openalex.org/W2998313947","https://openalex.org/W3004366655","https://openalex.org/W3012774908","https://openalex.org/W3045255111","https://openalex.org/W3109841242","https://openalex.org/W3148711710","https://openalex.org/W3152893301","https://openalex.org/W3155305352","https://openalex.org/W3204508881","https://openalex.org/W3210512903","https://openalex.org/W3215504775","https://openalex.org/W4220779330","https://openalex.org/W4224318899","https://openalex.org/W4243683928","https://openalex.org/W4246941204","https://openalex.org/W4252337780","https://openalex.org/W4282974123","https://openalex.org/W4282975557","https://openalex.org/W4285338635","https://openalex.org/W4285600519","https://openalex.org/W4285601728","https://openalex.org/W4296437558","https://openalex.org/W4367046696","https://openalex.org/W4367047306","https://openalex.org/W4383749341","https://openalex.org/W4385245566","https://openalex.org/W4385687011","https://openalex.org/W4391766344","https://openalex.org/W4392796768","https://openalex.org/W4393972734","https://openalex.org/W6607206061","https://openalex.org/W6640212811","https://openalex.org/W6733959537","https://openalex.org/W6738964360","https://openalex.org/W6744557953","https://openalex.org/W6745537798","https://openalex.org/W6749825310","https://openalex.org/W6751796012","https://openalex.org/W6754929296","https://openalex.org/W6757831817","https://openalex.org/W6762911047","https://openalex.org/W6766768147","https://openalex.org/W6771247989","https://openalex.org/W6773154947","https://openalex.org/W6774779085","https://openalex.org/W6776137863","https://openalex.org/W6779860234","https://openalex.org/W6783641038","https://openalex.org/W6789146462","https://openalex.org/W6794812656","https://openalex.org/W6800696080","https://openalex.org/W6810527119","https://openalex.org/W6840581513","https://openalex.org/W6844651315","https://openalex.org/W6845553907","https://openalex.org/W6846569231","https://openalex.org/W6849660393","https://openalex.org/W6849904387","https://openalex.org/W6870570394"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2147697413","https://openalex.org/W2154063878","https://openalex.org/W4231274751","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W3082059448","https://openalex.org/W4313640622"],"abstract_inverted_index":{"Learning":[0],"on":[1,49,158],"temporal":[2,33,111],"graphs":[3],"has":[4],"attracted":[5],"tremendous":[6],"research":[7],"interest":[8],"due":[9],"to":[10,29,39,84,147],"its":[11],"wide":[12],"range":[13],"of":[14,152],"applications.":[15],"Some":[16],"works":[17,37],"intuitively":[18],"merge":[19],"graph":[20,112,121],"neural":[21,26],"networks":[22,27],"(GNNs)":[23],"and":[24,32,35,66,78,102,109,127,136,162,181,189,201],"recurrent":[25],"(RNNs)":[28],"capture":[30],"structural":[31],"information,":[34],"recent":[36],"propose":[38,99],"aggregate":[40],"information":[41],"from":[42,60],"neighbor":[43],"nodes":[44,122],"in":[45,75,177],"local":[46,64,103,128,137],"subgraphs":[47],"based":[48],"message":[50],"passing":[51],"or":[52,63,87],"random":[53],"walks.":[54],"These":[55],"methods":[56],"produce":[57],"node":[58,153,183],"embeddings":[59,119,138],"a":[61,142,195],"global":[62,101,126,135],"perspective":[65],"ignore":[67],"the":[68,96,100],"complementarity":[69],"between":[70,198],"them,":[71],"thus":[72],"facing":[73],"limitations":[74],"capturing":[76],"complex":[77],"entangled":[79],"dynamic":[80,182],"patterns":[81],"when":[82],"applied":[83],"diverse":[85],"datasets":[86,161],"evaluated":[88],"by":[89,123,141],"more":[90,164],"challenging":[91],"evaluation":[92,166],"protocols.":[93],"To":[94],"address":[95],"issues,":[97],"we":[98],"embedding":[104],"network":[105],"(GLEN)":[106],"for":[107,120],"effective":[108,190],"efficient":[110],"representation":[113],"learning.":[114],"Specifically,":[115],"GLEN":[116,157,173,192],"dynamically":[117],"generates":[118],"considering":[124],"both":[125,178],"perspectives":[129],"using":[130],"specially":[131],"designed":[132],"modules.":[133],"Then,":[134],"are":[139],"combined":[140],"devised":[143],"cross-perspective":[144],"fusion":[145],"module":[146],"extract":[148],"high-order":[149],"semantic":[150],"relations":[151],"embeddings.":[154],"We":[155],"evaluate":[156],"multiple":[159],"real-world":[160],"apply":[163],"stringent":[165],"procedures.":[167],"Extensive":[168],"experimental":[169],"results":[170],"demonstrate":[171],"that":[172],"outperforms":[174],"other":[175],"baselines":[176],"link":[179],"prediction":[180],"classification":[184],"tasks.":[185],"Moreover,":[186],"with":[187],"concise":[188],"modules,":[191],"can":[193],"achieve":[194],"better":[196],"balance":[197],"inference":[199],"precision":[200],"training":[202],"efficiency.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}