{"id":"https://openalex.org/W4221126228","doi":"https://doi.org/10.1145/3488560.3498451","title":"EvoKG","display_name":"EvoKG","publication_year":2022,"publication_date":"2022-02-11","ids":{"openalex":"https://openalex.org/W4221126228","doi":"https://doi.org/10.1145/3488560.3498451"},"language":"en","primary_location":{"id":"doi:10.1145/3488560.3498451","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3488560.3498451","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3488560.3498451","source":{"id":"https://openalex.org/S4363608885","display_name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3488560.3498451","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072397278","display_name":"Namyong Park","orcid":"https://orcid.org/0000-0002-3344-2361"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Namyong Park","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024031732","display_name":"Fu\u2010Chen Liu","orcid":"https://orcid.org/0000-0001-9709-0556"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fuchen Liu","raw_affiliation_strings":["Microsoft, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056830370","display_name":"Purvanshi Mehta","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Purvanshi Mehta","raw_affiliation_strings":["Microsoft, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079286136","display_name":"Dana Cristofor","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dana Cristofor","raw_affiliation_strings":["Microsoft, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035605036","display_name":"Christos Faloutsos","orcid":"https://orcid.org/0000-0003-2996-9790"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Faloutsos","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052284218","display_name":"Yuxiao Dong","orcid":"https://orcid.org/0000-0002-6092-2002"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuxiao Dong","raw_affiliation_strings":["Microsoft, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":89,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"794","last_page":"803"},"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/T11719","display_name":"Data Quality and Management","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9944000244140625,"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":0.8512810468673706},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.790632963180542},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5838620066642761},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5412797927856445},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5234382152557373},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4946727454662323},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3872566521167755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8512810468673706},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.790632963180542},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5838620066642761},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5412797927856445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5234382152557373},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4946727454662323},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3872566521167755},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3488560.3498451","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3488560.3498451","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3488560.3498451","source":{"id":"https://openalex.org/S4363608885","display_name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2202.07648","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.07648","pdf_url":"https://arxiv.org/pdf/2202.07648","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3488560.3498451","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3488560.3498451","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3488560.3498451","source":{"id":"https://openalex.org/S4363608885","display_name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310207","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4221126228.pdf","grobid_xml":"https://content.openalex.org/works/W4221126228.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W804133461","https://openalex.org/W1533230146","https://openalex.org/W1888005072","https://openalex.org/W2024165284","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2154851992","https://openalex.org/W2440837865","https://openalex.org/W2509830164","https://openalex.org/W2538515886","https://openalex.org/W2565330852","https://openalex.org/W2728059831","https://openalex.org/W2773044566","https://openalex.org/W2787927827","https://openalex.org/W2798864014","https://openalex.org/W2798918712","https://openalex.org/W2806983170","https://openalex.org/W2889782235","https://openalex.org/W2890410208","https://openalex.org/W2907192581","https://openalex.org/W2911286998","https://openalex.org/W2922323458","https://openalex.org/W2922924332","https://openalex.org/W2927180808","https://openalex.org/W2949434543","https://openalex.org/W2954691982","https://openalex.org/W2962756421","https://openalex.org/W2962948632","https://openalex.org/W2964015378","https://openalex.org/W2965683718","https://openalex.org/W2972535098","https://openalex.org/W2998116985","https://openalex.org/W2998313947","https://openalex.org/W2998528434","https://openalex.org/W3003265726","https://openalex.org/W3007404067","https://openalex.org/W3012597271","https://openalex.org/W3035580113","https://openalex.org/W3097986917","https://openalex.org/W3101358844","https://openalex.org/W3101588560","https://openalex.org/W3104097132","https://openalex.org/W3111962319","https://openalex.org/W3161012769","https://openalex.org/W3187289530","https://openalex.org/W4287755062","https://openalex.org/W4288022080","https://openalex.org/W4288102558","https://openalex.org/W4294170691","https://openalex.org/W4297571622","https://openalex.org/W4297733535","https://openalex.org/W4299547686","https://openalex.org/W4300579117"],"related_works":["https://openalex.org/W2060561905","https://openalex.org/W1417711376","https://openalex.org/W2032260263","https://openalex.org/W1986883493","https://openalex.org/W2469862403","https://openalex.org/W2166378262","https://openalex.org/W2035891203","https://openalex.org/W4379524643","https://openalex.org/W2011027677","https://openalex.org/W2367807705"],"abstract_inverted_index":{"How":[0],"can":[1,54],"we":[2,76],"perform":[3],"knowledge":[4,8,66],"reasoning":[5,63,95],"over":[6,28,64,96],"temporal":[7,126,143],"graphs":[9],"(TKGs)?":[10],"TKGs":[11,129],"represent":[12],"facts":[13,33],"about":[14],"entities":[15,139],"and":[16,103,125,134,158,180,185,188],"their":[17],"relations,":[18],"where":[19],"each":[20],"fact":[21],"is":[22,37],"associated":[23],"with":[24],"a":[25,78],"timestamp.":[26],"Reasoning":[27],"TKGs,":[29,58,97],"i.e.,":[30],"inferring":[31],"new":[32],"from":[34],"time-evolving":[35],"KGs,":[36],"crucial":[38],"for":[39,92],"many":[40],"applications":[41],"to":[42,89,178],"provide":[43],"intelligent":[44],"services.":[45],"However,":[46],"despite":[47],"the":[48,83,100,104,122,136,142],"prevalence":[49],"of":[50,153,175],"real-world":[51],"data":[52],"that":[53,81,87,168],"be":[55,90],"represented":[56],"as":[57],"most":[59],"methods":[60,172],"focus":[61],"on":[62,141,162],"static":[65],"graphs,":[67],"or":[68],"cannot":[69],"predict":[70],"future":[71],"events.":[72],"In":[73],"this":[74],"paper,":[75],"present":[77],"problem":[79],"formulation":[80],"unifies":[82],"two":[84],"major":[85],"problems":[86],"need":[88],"addressed":[91],"an":[93,117,150],"effective":[94,118],"namely,":[98],"modeling":[99,152],"event":[101,132,154],"time":[102,184],"evolving":[105],"network":[106],"structure.":[107],"Our":[108],"proposed":[109],"method":[110],"EvoKG":[111,148,169],"jointly":[112],"models":[113,135],"both":[114],"tasks":[115],"in":[116,128,173],"framework,":[119],"which":[120],"captures":[121],"ever-changing":[123],"structural":[124],"dynamics":[127],"via":[130],"recurrent":[131],"modeling,":[133],"interactions":[137],"between":[138],"based":[140,161],"neighborhood":[144],"aggregation":[145],"framework.":[146],"Further,":[147],"achieves":[149],"accurate":[151,183],"time,":[155],"using":[156],"flexible":[157],"efficient":[159],"mechanisms":[160],"neural":[163],"density":[164],"estimation.":[165],"Experiments":[166],"show":[167],"outperforms":[170],"existing":[171],"terms":[174],"effectiveness":[176],"(up":[177],"77%":[179],"116%":[181],"more":[182],"link":[186],"prediction)":[187],"efficiency.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":15},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2022-04-03T00:00:00"}
