{"id":"https://openalex.org/W4383749430","doi":"https://doi.org/10.14778/3598581.3598583","title":"Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs","display_name":"Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs","publication_year":2023,"publication_date":"2023-05-01","ids":{"openalex":"https://openalex.org/W4383749430","doi":"https://doi.org/10.14778/3598581.3598583"},"language":"en","primary_location":{"id":"doi:10.14778/3598581.3598583","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3598581.3598583","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","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/A5046439802","display_name":"Janet Layne","orcid":"https://orcid.org/0000-0001-9533-5599"},"institutions":[{"id":"https://openalex.org/I120156002","display_name":"Boise State University","ror":"https://ror.org/02e3zdp86","country_code":"US","type":"education","lineage":["https://openalex.org/I120156002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Janet Layne","raw_affiliation_strings":["Boise State University, Boise, ID, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boise State University, Boise, ID, USA","institution_ids":["https://openalex.org/I120156002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075654046","display_name":"Justin Carpenter","orcid":null},"institutions":[{"id":"https://openalex.org/I120156002","display_name":"Boise State University","ror":"https://ror.org/02e3zdp86","country_code":"US","type":"education","lineage":["https://openalex.org/I120156002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Justin Carpenter","raw_affiliation_strings":["Boise State University, Boise, ID, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boise State University, Boise, ID, USA","institution_ids":["https://openalex.org/I120156002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009094578","display_name":"Edoardo Serra","orcid":"https://orcid.org/0000-0003-0689-5063"},"institutions":[{"id":"https://openalex.org/I120156002","display_name":"Boise State University","ror":"https://ror.org/02e3zdp86","country_code":"US","type":"education","lineage":["https://openalex.org/I120156002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edoardo Serra","raw_affiliation_strings":["Boise State University, Boise, ID, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boise State University, Boise, ID, USA","institution_ids":["https://openalex.org/I120156002"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026420819","display_name":"Francesco Gullo","orcid":"https://orcid.org/0000-0002-7052-1114"},"institutions":[{"id":"https://openalex.org/I153551853","display_name":"UniCredit (Italy)","ror":"https://ror.org/00fgmmg43","country_code":"IT","type":"company","lineage":["https://openalex.org/I153551853"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Gullo","raw_affiliation_strings":["UniCredit, Rome, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UniCredit, Rome, Italy","institution_ids":["https://openalex.org/I153551853"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.538,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.90947291,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"16","issue":"9","first_page":"2075","last_page":"2089"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"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":0.9991999864578247,"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/T10269","display_name":"Epigenetics and DNA Methylation","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9840999841690063,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.8282934427261353},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.711165189743042},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7050087451934814},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6426337957382202},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.6054503321647644},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6031527519226074},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5375857353210449},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5002317428588867},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.48118358850479126},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46054691076278687},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.4394541382789612},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.304053395986557},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28491339087486267}],"concepts":[{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.8282934427261353},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.711165189743042},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7050087451934814},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6426337957382202},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.6054503321647644},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6031527519226074},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5375857353210449},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5002317428588867},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.48118358850479126},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46054691076278687},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.4394541382789612},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.304053395986557},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28491339087486267},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3598581.3598583","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3598581.3598583","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":76,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2001141328","https://openalex.org/W2029766371","https://openalex.org/W2052104835","https://openalex.org/W2053186076","https://openalex.org/W2084809122","https://openalex.org/W2090891622","https://openalex.org/W2116341502","https://openalex.org/W2147991930","https://openalex.org/W2154851992","https://openalex.org/W2244843312","https://openalex.org/W2509830164","https://openalex.org/W2565330852","https://openalex.org/W2585835859","https://openalex.org/W2739747431","https://openalex.org/W2739994438","https://openalex.org/W2765652196","https://openalex.org/W2783466287","https://openalex.org/W2792234394","https://openalex.org/W2795735740","https://openalex.org/W2798918712","https://openalex.org/W2808087697","https://openalex.org/W2808771744","https://openalex.org/W2808908091","https://openalex.org/W2809156873","https://openalex.org/W2899831870","https://openalex.org/W2903329593","https://openalex.org/W2903383458","https://openalex.org/W2907492528","https://openalex.org/W2914999862","https://openalex.org/W2922924332","https://openalex.org/W2962756421","https://openalex.org/W2963224980","https://openalex.org/W2963555845","https://openalex.org/W2963885834","https://openalex.org/W2964811671","https://openalex.org/W2965683718","https://openalex.org/W2986068433","https://openalex.org/W2997042706","https://openalex.org/W2997485228","https://openalex.org/W2998116985","https://openalex.org/W2998496395","https://openalex.org/W2998528434","https://openalex.org/W3011667710","https://openalex.org/W3019863187","https://openalex.org/W3025882279","https://openalex.org/W3026076535","https://openalex.org/W3029916054","https://openalex.org/W3030895919","https://openalex.org/W3040043284","https://openalex.org/W3045255111","https://openalex.org/W3082891099","https://openalex.org/W3087775916","https://openalex.org/W3095494827","https://openalex.org/W3098366174","https://openalex.org/W3101358844","https://openalex.org/W3101444938","https://openalex.org/W3102794461","https://openalex.org/W3104097132","https://openalex.org/W3109841242","https://openalex.org/W3111962319","https://openalex.org/W3122089757","https://openalex.org/W3151791879","https://openalex.org/W3162323195","https://openalex.org/W3166605255","https://openalex.org/W3170430684","https://openalex.org/W3187582939","https://openalex.org/W3201293162","https://openalex.org/W3204508881","https://openalex.org/W3204742287","https://openalex.org/W3211774129","https://openalex.org/W4253848510","https://openalex.org/W4281811833","https://openalex.org/W4283366315","https://openalex.org/W4288080007","https://openalex.org/W4366492495"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W2912814903","https://openalex.org/W3206528106","https://openalex.org/W2950907416","https://openalex.org/W3038102983","https://openalex.org/W2082479932","https://openalex.org/W2123605750","https://openalex.org/W2932872266","https://openalex.org/W2088740331","https://openalex.org/W4281484020"],"abstract_inverted_index":{"Node":[0],"representation":[1,146],"learning":[2,147],"(NRL)":[3],"generates":[4],"numerical":[5],"vectors":[6],"(embeddings)":[7],"for":[8,21,50,148,169,184],"the":[9,59,91,95,101,163,195,208],"nodes":[10,23,57],"of":[11,58,103,116,127,165,178,199,210,243],"a":[12,69,140,166,174,200],"graph.":[13],"Structural":[14,46],"NRL":[15,47,78],"specifically":[16],"assigns":[17],"similar":[18,26],"node":[19,54,235],"embeddings":[20,40,96],"those":[22],"that":[24,151,219],"exhibit":[25],"structural":[27,63,77,105,131,145],"roles.":[28],"This":[29,181],"is":[30,48,182],"in":[31,82,90,114,207,234],"contrast":[32],"with":[33],"its":[34,222],"proximity-based":[35],"counterpart,":[36],"wherein":[37],"similarity":[38],"between":[39,74],"reflects":[41],"spatial":[42],"proximity":[43],"among":[44],"nodes.":[45],"useful":[49],"tasks":[51],"such":[52],"as":[53,190],"classification":[55,236],"where":[56],"same":[60],"class":[61],"share":[62],"roles,":[64],"though":[65],"there":[66],"may":[67],"exist":[68],"distant,":[70],"or":[71,129],"no":[72],"path":[73],"them.":[75],"Athough":[76],"has":[79,86],"been":[80],"well-studied":[81],"static":[83],"graphs,":[84],"it":[85],"received":[87],"limited":[88,113],"attention":[89],"temporal":[92,149,176,212],"setting.":[93],"Here,":[94],"are":[97,112],"required":[98],"to":[99,123,144,191,246],"represent":[100],"evolution":[102],"nodes'":[104],"roles":[106],"over":[107],"time.":[108],"The":[109],"existing":[110],"methods":[111],"terms":[115],"efficiency":[117],"and":[118,214,228,237,240],"effectiveness:":[119],"they":[120],"scale":[121],"poorly":[122],"even":[124],"moderate":[125],"number":[126,209],"timestamps,":[128],"capture":[130],"role":[132],"only":[133],"tangentially.":[134],"In":[135],"this":[136],"work,":[137],"we":[138],"present":[139],"novel":[141],"unsupervised":[142],"approach":[143,159,203,245],"graphs":[150],"overcomes":[152],"these":[153],"limitations.":[154],"For":[155],"each":[156,170],"node,":[157],"our":[158,244],"clusters":[160],"then":[161],"aggregates":[162],"embedding":[164],"node's":[167],"neighbors":[168],"timestamp,":[171],"followed":[172],"by":[173],"further":[175],"aggregation":[177],"all":[179],"timestamps.":[180],"repeated":[183],"(at":[185],"most)":[186],"d":[187,196],"iterations,":[188],"so":[189],"acquire":[192],"information":[193],"from":[194],"-hop":[197],"neighborhood":[198],"node.":[201],"Our":[202],"takes":[204],"linear":[205],"time":[206],"overall":[211],"edges,":[213],"possesses":[215],"important":[216],"theoretical":[217],"properties":[218],"formally":[220],"demonstrate":[221],"effectiveness.":[223],"Extensive":[224],"experiments":[225],"on":[226],"synthetic":[227],"real":[229],"datasets":[230],"show":[231],"superior":[232,241],"performance":[233],"regression":[238],"tasks,":[239],"scalability":[242],"large":[247],"graphs.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
