{"id":"https://openalex.org/W3156926010","doi":"https://doi.org/10.1145/3442381.3449921","title":"Highly Liquid Temporal Interaction Graph Embeddings","display_name":"Highly Liquid Temporal Interaction Graph Embeddings","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3156926010","doi":"https://doi.org/10.1145/3442381.3449921","mag":"3156926010"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3449921","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449921","pdf_url":null,"source":null,"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 Web Conference 2021","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3449921","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054365307","display_name":"Huidi Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huidi Chen","raw_affiliation_strings":["Fudan University, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001877137","display_name":"Yun Xiong","orcid":"https://orcid.org/0000-0002-8575-5415"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Xiong","raw_affiliation_strings":["Fudan University and Shanghai Institute for Advanced Communication and Data Science, China"],"affiliations":[{"raw_affiliation_string":"Fudan University and Shanghai Institute for Advanced Communication and Data Science, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020370754","display_name":"Yangyong Zhu","orcid":"https://orcid.org/0000-0002-6258-0747"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangyong Zhu","raw_affiliation_strings":["Fudan University and Shanghai Institute for Advanced Communication and Data Science, China"],"affiliations":[{"raw_affiliation_string":"Fudan University and Shanghai Institute for Advanced Communication and Data Science, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036357902","display_name":"Philip S. Yu","orcid":"https://orcid.org/0000-0002-3491-5968"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip S. Yu","raw_affiliation_strings":["University of Illinois at Chicago, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, USA","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5054365307"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":1.4956,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.85354852,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1639","last_page":"1648"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987000226974487,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9943000078201294,"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/computer-science","display_name":"Computer science","score":0.7063302993774414},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.695054292678833},{"id":"https://openalex.org/keywords/timeline","display_name":"Timeline","score":0.6279118061065674},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.5408840775489807},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5363430380821228},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5293805003166199},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5227712988853455},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4736184775829315},{"id":"https://openalex.org/keywords/interaction-information","display_name":"Interaction information","score":0.45914599299430847},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3923704922199249},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25597071647644043},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18196654319763184}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7063302993774414},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.695054292678833},{"id":"https://openalex.org/C4438859","wikidata":"https://www.wikidata.org/wiki/Q186117","display_name":"Timeline","level":2,"score":0.6279118061065674},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.5408840775489807},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5363430380821228},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5293805003166199},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5227712988853455},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4736184775829315},{"id":"https://openalex.org/C38764148","wikidata":"https://www.wikidata.org/wiki/Q17098245","display_name":"Interaction information","level":2,"score":0.45914599299430847},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3923704922199249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25597071647644043},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18196654319763184},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3442381.3449921","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449921","pdf_url":null,"source":null,"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 Web Conference 2021","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3442381.3449921","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449921","pdf_url":null,"source":null,"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 Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1921081011","https://openalex.org/W2017102965","https://openalex.org/W2089554624","https://openalex.org/W2102848467","https://openalex.org/W2154851992","https://openalex.org/W2250539671","https://openalex.org/W2251415563","https://openalex.org/W2569842309","https://openalex.org/W2583674722","https://openalex.org/W2734601503","https://openalex.org/W2739805805","https://openalex.org/W2742491462","https://openalex.org/W2747329762","https://openalex.org/W2767597557","https://openalex.org/W2770628529","https://openalex.org/W2773640334","https://openalex.org/W2786915849","https://openalex.org/W2792839191","https://openalex.org/W2795466160","https://openalex.org/W2886620073","https://openalex.org/W2911286998","https://openalex.org/W2922520266","https://openalex.org/W2935213508","https://openalex.org/W2962767366","https://openalex.org/W2963403868","https://openalex.org/W2963858333","https://openalex.org/W2963869731","https://openalex.org/W2965410650","https://openalex.org/W2965683718","https://openalex.org/W3098087397","https://openalex.org/W3101588560","https://openalex.org/W3102201080","https://openalex.org/W3104097132","https://openalex.org/W3125626040"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Capturing":[0],"the":[1,26,31,35,47,53,57,72,128,131,135,150,181,188,203,211,214,217],"topological":[2],"and":[3,8,23,38,121,154,159,192],"temporal":[4,102,226],"information":[5,49,60,70,78,85,110,114],"of":[6,28,50,59,84,130,184,187,206,216],"interactions":[7,11],"predicting":[9],"future":[10],"are":[12,40,124],"crucial":[13],"for":[14],"many":[15],"domains,":[16],"such":[17],"as":[18],"social":[19],"networks,":[20],"financial":[21],"transactions,":[22],"e-commerce.":[24],"With":[25],"advent":[27],"co-evolutional":[29],"models,":[30],"mutual":[32],"influence":[33],"between":[34,152],"interacted":[36],"users":[37],"items":[39],"captured.":[41],"However,":[42],"existing":[43],"models":[44],"only":[45],"update":[46,155],"interaction":[48,103,109,137,227],"nodes":[51,65,132,153],"along":[52],"timeline.":[54],"It":[55],"causes":[56],"problem":[58],"asymmetry,":[61],"where":[62],"early":[63],"updated":[64,75],"often":[66],"have":[67],"much":[68],"less":[69],"than":[71],"most":[73],"recently":[74,119],"nodes.":[76,200],"The":[77,146],"asymmetry":[79],"is":[80],"essentially":[81],"a":[82,166,169,174,207],"blockage":[83],"flow.":[86],"We":[87,201],"propose":[88],"HILI":[89,139,163],"(Highly":[90],"Liquid":[91],"Temporal":[92],"Interaction":[93],"Graph":[94],"Embeddings)":[95],"to":[96,126],"predict":[97],"highly":[98,111],"liquid":[99,112],"embeddings":[100,142],"on":[101,198],"graphs.":[104],"Our":[105],"embedding":[106,157,186,195],"model":[107,222],"makes":[108,193],"without":[113],"asymmetry.":[115],"A":[116,177],"specific":[117],"least":[118],"used-based":[120],"frequency-based":[122],"windows":[123],"used":[125],"determine":[127],"priority":[129],"that":[133,220],"receive":[134],"latest":[136],"information.":[138],"updates":[140],"node":[141,156,191],"by":[143],"attention":[144,147],"layers.":[145],"layers":[148],"learn":[149],"correlation":[151],"simply":[158],"quickly.":[160],"In":[161],"addition,":[162],"elaborately":[164],"designs,":[165],"self-linear":[167,178,208],"layer,":[168],"linear":[170],"layer":[171,179,209],"initialized":[172],"in":[173,210],"novel":[175],"method.":[176],"reduces":[180],"expected":[182],"space":[183],"predicted":[185,194],"next":[189],"interacting":[190],"focus":[196],"more":[197],"relevant":[199],"illustrate":[202],"geometric":[204],"meaning":[205],"paper.":[212],"Furthermore,":[213],"results":[215],"experiments":[218],"show":[219],"our":[221],"outperforms":[223],"other":[224],"state-of-the-art":[225],"prediction":[228],"models.":[229]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
