{"id":"https://openalex.org/W3033917965","doi":"https://doi.org/10.1145/3391298","title":"Edge2vec","display_name":"Edge2vec","publication_year":2020,"publication_date":"2020-05-30","ids":{"openalex":"https://openalex.org/W3033917965","doi":"https://doi.org/10.1145/3391298","mag":"3033917965"},"language":"en","primary_location":{"id":"doi:10.1145/3391298","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3391298","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3391298","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"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":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3391298","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113610809","display_name":"Changping Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changping Wang","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047153705","display_name":"Chaokun Wang","orcid":"https://orcid.org/0000-0002-2986-2574"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaokun Wang","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401045","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0001-6157-0662"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101655873","display_name":"Xiaojun Ye","orcid":"https://orcid.org/0000-0002-9780-4827"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Ye","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"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"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5113610809"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.3979,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.93749593,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"14","issue":"4","first_page":"1","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994999766349792,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9277999997138977,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7120670080184937},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.654669463634491},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6081830263137817},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5479575991630554},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5250396728515625},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5024840831756592},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.46904951333999634},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4645775258541107},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.4520038962364197},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37963035702705383},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3211125135421753},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32022377848625183},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2779621481895447}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7120670080184937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.654669463634491},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6081830263137817},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5479575991630554},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5250396728515625},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5024840831756592},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.46904951333999634},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4645775258541107},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4520038962364197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37963035702705383},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3211125135421753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32022377848625183},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2779621481895447},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3391298","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3391298","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3391298","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"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":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3391298","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3391298","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3391298","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"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":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G1481410601","display_name":null,"funder_award_id":"III-1526499","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2142201510","display_name":"III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations","funder_award_id":"1909323","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3118254216","display_name":"III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation","funder_award_id":"1763325","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4557816978","display_name":null,"funder_award_id":"61872207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7304636144","display_name":null,"funder_award_id":"CNS-1930941","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7633400475","display_name":null,"funder_award_id":"III-1763325","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G779482199","display_name":"III: Small: Fusion of Heterogeneous Networks for Synergistic Knowledge Discovery","funder_award_id":"1526499","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7817793019","display_name":null,"funder_award_id":"III-1909323","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G979281235","display_name":"SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers","funder_award_id":"1930941","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3033917965.pdf","grobid_xml":"https://content.openalex.org/works/W3033917965.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W34646664","https://openalex.org/W1888005072","https://openalex.org/W1980672078","https://openalex.org/W1985514943","https://openalex.org/W2001141328","https://openalex.org/W2010627475","https://openalex.org/W2013208096","https://openalex.org/W2018749988","https://openalex.org/W2053186076","https://openalex.org/W2073415627","https://openalex.org/W2084445547","https://openalex.org/W2090891622","https://openalex.org/W2093882311","https://openalex.org/W2101409529","https://openalex.org/W2124142520","https://openalex.org/W2136116685","https://openalex.org/W2154851992","https://openalex.org/W2155957339","https://openalex.org/W2156273451","https://openalex.org/W2166293769","https://openalex.org/W2241862190","https://openalex.org/W2247119764","https://openalex.org/W2393319904","https://openalex.org/W2420733993","https://openalex.org/W2511871973","https://openalex.org/W2517425990","https://openalex.org/W2560674852","https://openalex.org/W2604942799","https://openalex.org/W2608239929","https://openalex.org/W2614812929","https://openalex.org/W2913932916","https://openalex.org/W2914304175","https://openalex.org/W2962756421","https://openalex.org/W3015364301","https://openalex.org/W3104097132","https://openalex.org/W3104211877","https://openalex.org/W3105705953","https://openalex.org/W3122868618","https://openalex.org/W4301113013"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W2939353110","https://openalex.org/W4287763734","https://openalex.org/W3035116611","https://openalex.org/W3094605108","https://openalex.org/W3044354590","https://openalex.org/W2923818335","https://openalex.org/W4212923699","https://openalex.org/W2893186803","https://openalex.org/W4310879833"],"abstract_inverted_index":{"Graph":[0],"embedding,":[1],"also":[2],"known":[3],"as":[4,149,151],"network":[5,8,24,48,95],"embedding":[6,22,32,112],"and":[7,98,133,164],"representation":[9],"learning,":[10],"is":[11,65,74,96,105],"a":[12,23,26,47,168],"useful":[13],"technique":[14],"which":[15,37],"helps":[16],"researchers":[17],"analyze":[18],"information":[19,137,145,190],"networks":[20,123],"through":[21,167],"into":[25,140],"low-dimensional":[27,50,126],"space.":[28],"However,":[29],"existing":[30],"graph":[31,111],"methods":[33],"are":[34],"all":[35],"node-based,":[36],"means":[38],"they":[39],"can":[40],"just":[41],"directly":[42,124],"map":[43,118],"the":[44,53,66,70,79,94,100,119,131,134,161,183,188],"nodes":[45,104],"of":[46,72,81,90,103,138,146,191],"to":[49,59,117,125,142],"vectors":[51,60],"while":[52],"edges":[54,73,120,139,148],"could":[55],"only":[56],"be":[57],"mapped":[58],"indirectly.":[61],"One":[62],"important":[63,88],"reason":[64],"computational":[67],"cost,":[68],"because":[69],"number":[71,80],"always":[75],"far":[76],"greater":[77],"than":[78],"nodes.":[82],"In":[83],"this":[84,155],"article,":[85],"considering":[86],"an":[87,109],"property":[89],"social":[91,122],"networks,":[92],"i.e.,":[93],"sparse,":[97],"hence":[99],"average":[101],"degree":[102],"bounded,":[106],"we":[107],"propose":[108],"edge-based":[110],"(":[113],"edge2vec":[114,157,180],")":[115],"method":[116],"in":[121,186],"vectors.":[127],"Edge2vec":[128],"takes":[129],"both":[130],"local":[132],"global":[135],"structure":[136,144,189],"consideration":[141],"preserve":[143],"embedded":[147],"much":[150],"possible.":[152],"To":[153],"achieve":[154],"goal,":[156],"first":[158],"ingeniously":[159],"combines":[160],"deep":[162,170],"autoencoder":[163],"Skip-gram":[165],"model":[166],"well-designed":[169],"neural":[171],"network.":[172],"The":[173],"experimental":[174],"results":[175],"on":[176],"different":[177],"datasets":[178],"show":[179],"benefits":[181],"from":[182],"direct":[184],"mapping":[185],"preserving":[187],"edges.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2020-06-12T00:00:00"}