{"id":"https://openalex.org/W4306317244","doi":"https://doi.org/10.1145/3511808.3557664","title":"OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network","display_name":"OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317244","doi":"https://doi.org/10.1145/3511808.3557664"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557664","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557664","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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/A5101580391","display_name":"Hui Han","orcid":"https://orcid.org/0000-0002-6295-5908"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hui Han","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032924599","display_name":"Tianyu Zhao","orcid":"https://orcid.org/0000-0003-0356-1244"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Zhao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060417049","display_name":"Cheng Yang","orcid":"https://orcid.org/0000-0001-7821-0030"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications &amp; Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications &amp; Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081660853","display_name":"Zhang Hongyi","orcid":"https://orcid.org/0009-0000-5571-4834"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyi Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027437026","display_name":"Yaoqi Liu","orcid":"https://orcid.org/0009-0003-5841-9517"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaoqi Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411469","display_name":"Xiao Wang","orcid":"https://orcid.org/0000-0002-4444-7811"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications &amp; Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications &amp; Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100705849","display_name":"Chuan Shi","orcid":"https://orcid.org/0000-0002-3734-0266"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Shi","raw_affiliation_strings":["Beijing University of Posts and Telecommunications &amp; Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications &amp; Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101580391"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":4.0914,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.95283019,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3993","last_page":"3997"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9902999997138977,"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"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9884999990463257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8685548305511475},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.714274525642395},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5783576965332031},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5183321237564087},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.5176406502723694},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4278336763381958},{"id":"https://openalex.org/keywords/open-source","display_name":"Open source","score":0.41128796339035034},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37827786803245544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36781564354896545},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.3527357280254364},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34999537467956543},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3435899019241333},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.27283549308776855},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.19263723492622375}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8685548305511475},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.714274525642395},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5783576965332031},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5183321237564087},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.5176406502723694},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4278336763381958},{"id":"https://openalex.org/C3018397939","wikidata":"https://www.wikidata.org/wiki/Q3644502","display_name":"Open source","level":3,"score":0.41128796339035034},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37827786803245544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36781564354896545},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.3527357280254364},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34999537467956543},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3435899019241333},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.27283549308776855},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.19263723492622375},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557664","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557664","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2551706664","https://openalex.org/W2743159750","https://openalex.org/W2889583850","https://openalex.org/W2949676527","https://openalex.org/W2951626319","https://openalex.org/W2963707260","https://openalex.org/W2963919031","https://openalex.org/W2965857891","https://openalex.org/W3004507689","https://openalex.org/W3098230582","https://openalex.org/W3103513278","https://openalex.org/W3108202858","https://openalex.org/W3114303065","https://openalex.org/W3206089854","https://openalex.org/W3209009171","https://openalex.org/W3209580887","https://openalex.org/W3211000797","https://openalex.org/W4226204438","https://openalex.org/W6785059380"],"related_works":["https://openalex.org/W3022194174","https://openalex.org/W2377966044","https://openalex.org/W3038868399","https://openalex.org/W2612710542","https://openalex.org/W2123834217","https://openalex.org/W4283511977","https://openalex.org/W4323035947","https://openalex.org/W4287691332","https://openalex.org/W190270261","https://openalex.org/W3048169489"],"abstract_inverted_index":{"Heterogeneous":[0],"Graph":[1],"Neural":[2],"Networks":[3],"(HGNNs),":[4],"as":[5,116],"a":[6,70,85,88],"kind":[7],"of":[8,21,130],"powerful":[9],"graph":[10,105],"representation":[11],"learning":[12],"methods":[13],"on":[14,46,87],"heterogeneous":[15,104],"graphs,":[16],"have":[17,28],"attracted":[18],"increasing":[19],"attention":[20],"many":[22],"researchers.":[23],"Although,":[24],"several":[25,144],"existing":[26],"libraries":[27],"supported":[29],"HGNNs,":[30],"they":[31],"just":[32,92],"provide":[33,159],"the":[34,127,171],"most":[35],"basic":[36],"models":[37],"and":[38,41,51,72,77,102,121,146,149,156,170],"operators.":[39],"Building":[40],"benchmarking":[42],"various":[43,112],"downstream":[44],"tasks":[45],"HGNNs":[47,101],"is":[48,166,174],"still":[49],"painful":[50],"time":[52],"consuming":[53],"with":[54,91,161],"them.":[55],"In":[56,123],"this":[57],"paper,":[58],"we":[59],"will":[60],"introduce":[61],"OpenHGNN,":[62,131],"an":[63,167],"open-source":[64,168],"toolkit":[65],"for":[66,75,111],"HGNNs.":[67],"OpenHGNN":[68,96,165],"defines":[69],"unified":[71],"standard":[73],"pipeline":[74],"training":[76],"testing,":[78],"which":[79,107],"can":[80,108,133],"allow":[81],"users":[82,160],"to":[83,126,136,158],"run":[84],"model":[86],"specific":[89],"dataset":[90],"one":[93],"command":[94],"line.":[95],"has":[97],"integrated":[98],"20+":[99,103],"mainstream":[100],"datasets,":[106],"be":[109,134],"used":[110],"advanced":[113],"tasks,":[114],"such":[115],"node":[117],"classification,":[118],"link":[119],"prediction,":[120],"recommendation.":[122],"addition,":[124],"thanks":[125],"modularized":[128],"design":[129,154],"it":[132],"extended":[135],"meet":[137],"users'":[138],"customized":[139],"needs.":[140],"We":[141],"also":[142],"release":[143],"novel":[145],"useful":[147],"tools":[148],"features,":[150],"including":[151],"leaderboard,":[152],"autoML,":[153],"space,":[155],"visualization,":[157],"better":[162],"usage":[163],"experiences.":[164],"project,":[169],"source":[172],"code":[173],"available":[175],"at":[176],"https://github.com/BUPT-GAMMA/OpenHGNN.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":11}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
