{"id":"https://openalex.org/W4409149345","doi":"https://doi.org/10.1145/3690624.3709251","title":"Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes","display_name":"Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409149345","doi":"https://doi.org/10.1145/3690624.3709251"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709251","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-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/A5110220048","display_name":"Yige Zhao","orcid":"https://orcid.org/0009-0001-6195-1665"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yige Zhao","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102170154","display_name":"Jianxiang Yu","orcid":"https://orcid.org/0009-0006-9900-9815"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxiang Yu","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019567153","display_name":"Yao Cheng","orcid":"https://orcid.org/0000-0001-9179-7032"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Cheng","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041949740","display_name":"Chengcheng Yu","orcid":"https://orcid.org/0000-0001-9486-1247"},"institutions":[{"id":"https://openalex.org/I135905480","display_name":"Shanghai Polytechnic University","ror":"https://ror.org/02as5yg64","country_code":"CN","type":"education","lineage":["https://openalex.org/I135905480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengcheng Yu","raw_affiliation_strings":["Shanghai Polytechnic University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Polytechnic University, Shanghai, China","institution_ids":["https://openalex.org/I135905480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101677601","display_name":"Yiding Liu","orcid":"https://orcid.org/0000-0001-6857-261X"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiding Liu","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092189676","display_name":"Xiang Li","orcid":"https://orcid.org/0009-0003-0142-2483"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050255638","display_name":"Shuaiqiang Wang","orcid":"https://orcid.org/0000-0002-9212-1947"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuaiqiang Wang","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5110220048"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":2.4849,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88914862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2067","last_page":"2078"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9987999796867371,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/autoencoder","display_name":"Autoencoder","score":0.8965545892715454},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5933343172073364},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5264835357666016},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4662166237831116},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.42732858657836914},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33335697650909424},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.31795692443847656},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.28580838441848755},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2171829342842102},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.12095093727111816}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8965545892715454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5933343172073364},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5264835357666016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4662166237831116},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.42732858657836914},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33335697650909424},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31795692443847656},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28580838441848755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2171829342842102},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.12095093727111816}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709251","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W2049137142","https://openalex.org/W2393319904","https://openalex.org/W2743104969","https://openalex.org/W2903634148","https://openalex.org/W2908461307","https://openalex.org/W2911286998","https://openalex.org/W2952343887","https://openalex.org/W2963919031","https://openalex.org/W2965857891","https://openalex.org/W3006946324","https://openalex.org/W3012871709","https://openalex.org/W3016163458","https://openalex.org/W3021975806","https://openalex.org/W3038342492","https://openalex.org/W3040731923","https://openalex.org/W3042085764","https://openalex.org/W3094678421","https://openalex.org/W3095602948","https://openalex.org/W3095746859","https://openalex.org/W3100078588","https://openalex.org/W3134210100","https://openalex.org/W3154503084","https://openalex.org/W3155886566","https://openalex.org/W3163416963","https://openalex.org/W3172710079","https://openalex.org/W3176047499","https://openalex.org/W3204453541","https://openalex.org/W4220672543","https://openalex.org/W4290948206","https://openalex.org/W4367047181","https://openalex.org/W4382202833","https://openalex.org/W6600120041","https://openalex.org/W6600266142","https://openalex.org/W6755573351","https://openalex.org/W6780120487","https://openalex.org/W6784694379","https://openalex.org/W6792108999","https://openalex.org/W6814250579"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2891286602","https://openalex.org/W2532801570","https://openalex.org/W4385627933","https://openalex.org/W2480127678","https://openalex.org/W2806270048","https://openalex.org/W4310605282"],"abstract_inverted_index":{"Heterogeneous":[0],"Information":[1],"Networks":[2],"(HINs),":[3],"which":[4,44,97,138],"consist":[5],"of":[6,9,35,117,141,176],"various":[7],"types":[8],"nodes":[10,69,143],"and":[11,40,91,114,183,188],"edges,":[12],"have":[13],"recently":[14],"witnessed":[15],"excellent":[16],"performance":[17],"in":[18,70,94,178],"graph":[19,25,72,84],"mining.":[20],"However,":[21],"most":[22],"existing":[23],"heterogeneous":[24],"neural":[26],"networks":[27],"(HGNNs)":[28],"fail":[29],"to":[30,58,103,172],"simultaneously":[31],"handle":[32],"the":[33,68,71,82,95,108,127,134,174],"problems":[34],"missing":[36,182],"attributes,":[37],"inaccurate":[38,162,184],"attributes":[39],"scarce":[41],"node":[42,105],"labels,":[43],"limits":[45],"their":[46],"expressiveness.":[47],"In":[48,107,147],"this":[49,148],"paper,":[50],"we":[51,168],"propose":[52],"a":[53,74],"generative":[54],"self-supervised":[55],"model":[56],"GraMI":[57,64,87,110,125,150,177],"address":[59],"these":[60],"issues":[61],"simultaneously.":[62],"Specifically,":[63],"first":[65],"initializes":[66],"all":[67,133],"with":[73,181],"low-dimensional":[75,129],"representation":[76,130],"matrix.":[77],"After":[78],"that,":[79],"based":[80,136],"on":[81,137],"variational":[83],"autoencoder":[85],"framework,":[86],"learns":[88],"both":[89,112],"node-level":[90],"attribute-level":[92],"embeddings":[93],"encoder,":[96],"can":[98,151,190],"provide":[99],"fine-grained":[100],"semantic":[101],"information":[102],"construct":[104],"attributes.":[106,115,185],"decoder,":[109],"reconstructs":[111],"links":[113],"Instead":[116],"directly":[118],"reconstructing":[119],"raw":[120,139],"features":[121,140,156],"for":[122,132,157,164],"attributed":[123,142,165],"nodes,":[124,135,159],"generates":[126],"initial":[128],"matrix":[131],"are":[144],"further":[145],"reconstructed.":[146],"way,":[149],"not":[152],"only":[153],"complete":[154],"informative":[155],"non-attributed":[158],"but":[160],"rectify":[161],"ones":[163],"nodes.":[166],"Finally,":[167],"conduct":[169],"extensive":[170],"experiments":[171],"show":[173],"superiority":[175],"tackling":[179],"HINs":[180],"Our":[186],"code":[187],"data":[189],"be":[191],"found":[192],"here:":[193],"https://github.com/See-r/GraMI.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
