{"id":"https://openalex.org/W4401478693","doi":"https://doi.org/10.1007/s13042-024-02305-1","title":"RHGNN: imposing relational inductive bias for heterogeneous graph neural network","display_name":"RHGNN: imposing relational inductive bias for heterogeneous graph neural network","publication_year":2024,"publication_date":"2024-08-10","ids":{"openalex":"https://openalex.org/W4401478693","doi":"https://doi.org/10.1007/s13042-024-02305-1"},"language":"en","primary_location":{"id":"doi:10.1007/s13042-024-02305-1","is_oa":false,"landing_page_url":"https://doi.org/10.1007/s13042-024-02305-1","pdf_url":null,"source":{"id":"https://openalex.org/S2764999920","display_name":"International Journal of Machine Learning and Cybernetics","issn_l":"1868-8071","issn":["1868-8071","1868-808X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Machine Learning and Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://hdl.handle.net/10072/432730","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101470025","display_name":"Shichao Zhu","orcid":"https://orcid.org/0000-0003-1776-8983"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shichao Zhu","raw_affiliation_strings":["ByteDance, Beijing, 100190, China"],"affiliations":[{"raw_affiliation_string":"ByteDance, Beijing, 100190, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036186751","display_name":"Shuai Zhang","orcid":"https://orcid.org/0009-0009-1288-5460"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210120485","display_name":"Academy of Mathematics and Systems Science","ror":"https://ror.org/02jkmyk67","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210120485"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Zhang","raw_affiliation_strings":["Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China"],"affiliations":[{"raw_affiliation_string":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China","institution_ids":["https://openalex.org/I4210120485","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100358862","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-1187-6388"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210120485","display_name":"Academy of Mathematics and Systems Science","ror":"https://ror.org/02jkmyk67","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210120485"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China"],"affiliations":[{"raw_affiliation_string":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China","institution_ids":["https://openalex.org/I4210120485","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085319576","display_name":"Chuan Zhou","orcid":"https://orcid.org/0000-0001-9958-8673"},"institutions":[{"id":"https://openalex.org/I4210120485","display_name":"Academy of Mathematics and Systems Science","ror":"https://ror.org/02jkmyk67","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210120485"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Zhou","raw_affiliation_strings":["Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China","School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100049, China"],"affiliations":[{"raw_affiliation_string":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China","institution_ids":["https://openalex.org/I4210120485","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008056593","display_name":"Shirui Pan","orcid":"https://orcid.org/0000-0003-0794-527X"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shirui Pan","raw_affiliation_strings":["Griffith University, Queensland, 4222, Australia"],"affiliations":[{"raw_affiliation_string":"Griffith University, Queensland, 4222, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006025566","display_name":"Li Zhao","orcid":"https://orcid.org/0000-0001-7374-3348"},"institutions":[{"id":"https://openalex.org/I4210091192","display_name":"Zhejiang Zanyu Technology (China)","ror":"https://ror.org/00e58e871","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091192"]},{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Li","raw_affiliation_strings":["Hangzhou Yugu Technology Co., Ltd., Hangzhou, 311113, China","Research Center for Graph Computing, Zhejiang Lab, Hangzhou, 311121, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Yugu Technology Co., Ltd., Hangzhou, 311113, China","institution_ids":["https://openalex.org/I4210091192"]},{"raw_affiliation_string":"Research Center for Graph Computing, Zhejiang Lab, Hangzhou, 311121, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008473103","display_name":"Hongyang Chen","orcid":"https://orcid.org/0000-0002-7626-0162"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyang Chen","raw_affiliation_strings":["Research Center for Graph Computing, Zhejiang Lab, Hangzhou, 311121, China"],"affiliations":[{"raw_affiliation_string":"Research Center for Graph Computing, Zhejiang Lab, Hangzhou, 311121, China","institution_ids":["https://openalex.org/I4210123185"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101470025"],"corresponding_institution_ids":[],"apc_list":{"value":2790,"currency":"EUR","value_usd":3590},"apc_paid":{"value":2790,"currency":"EUR","value_usd":3590},"fwci":2.5353,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.90685242,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"16","issue":"2","first_page":"855","last_page":"871"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.983299970626831,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9740999937057495,"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":0.7336740493774414},{"id":"https://openalex.org/keywords/inductive-bias","display_name":"Inductive bias","score":0.6013089418411255},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5766409039497375},{"id":"https://openalex.org/keywords/heterogeneous-network","display_name":"Heterogeneous network","score":0.5475202798843384},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5042098760604858},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35926273465156555},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.13200515508651733}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7336740493774414},{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.6013089418411255},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5766409039497375},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.5475202798843384},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5042098760604858},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35926273465156555},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.13200515508651733},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s13042-024-02305-1","is_oa":false,"landing_page_url":"https://doi.org/10.1007/s13042-024-02305-1","pdf_url":null,"source":{"id":"https://openalex.org/S2764999920","display_name":"International Journal of Machine Learning and Cybernetics","issn_l":"1868-8071","issn":["1868-8071","1868-808X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Machine Learning and Cybernetics","raw_type":"journal-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/432730","is_oa":true,"landing_page_url":"https://hdl.handle.net/10072/432730","pdf_url":null,"source":null,"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal article"}],"best_oa_location":{"id":"pmh:oai:research-repository.griffith.edu.au:10072/432730","is_oa":true,"landing_page_url":"https://hdl.handle.net/10072/432730","pdf_url":null,"source":null,"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1506109531","https://openalex.org/W1689688298","https://openalex.org/W1849400742","https://openalex.org/W2062797058","https://openalex.org/W2075010670","https://openalex.org/W2087159174","https://openalex.org/W2145658888","https://openalex.org/W2523679382","https://openalex.org/W2600702321","https://openalex.org/W2604314403","https://openalex.org/W2605181250","https://openalex.org/W2743104969","https://openalex.org/W2767774008","https://openalex.org/W2768308213","https://openalex.org/W2807021761","https://openalex.org/W2896161497","https://openalex.org/W2911286998","https://openalex.org/W2945266622","https://openalex.org/W2950393809","https://openalex.org/W2951626319","https://openalex.org/W2962756421","https://openalex.org/W2963406064","https://openalex.org/W2963707260","https://openalex.org/W2963919031","https://openalex.org/W2964700358","https://openalex.org/W2982438846","https://openalex.org/W3003795821","https://openalex.org/W3012871709","https://openalex.org/W3042807565","https://openalex.org/W3100848837","https://openalex.org/W3152893301","https://openalex.org/W3165439069","https://openalex.org/W3217103056","https://openalex.org/W4210334699","https://openalex.org/W4210746245","https://openalex.org/W4213086502","https://openalex.org/W4306663340","https://openalex.org/W4312331995","https://openalex.org/W4361214937","https://openalex.org/W4382237536","https://openalex.org/W4387164576","https://openalex.org/W4395097571","https://openalex.org/W6600239251","https://openalex.org/W6600577311","https://openalex.org/W6605533441"],"related_works":["https://openalex.org/W1810370127","https://openalex.org/W1927327903","https://openalex.org/W1544665982","https://openalex.org/W2022479666","https://openalex.org/W1594946127","https://openalex.org/W4312273141","https://openalex.org/W2805504328","https://openalex.org/W4248847438","https://openalex.org/W2509090697","https://openalex.org/W1953159635"],"abstract_inverted_index":{"Heterogeneous":[0],"graph":[1,90,105,155],"data":[2],"are":[3],"ubiquitous":[4],"and":[5,42,70,78,113,121,138,142,160,166,172,222],"extracting":[6],"information":[7,41,144,164],"from":[8],"them":[9],"is":[10],"increasingly":[11],"crucial.":[12],"Existing":[13],"approaches":[14],"for":[15,63,119,169],"modeling":[16,102],"heterogeneous":[17,46,64,89,104,148,154],"graphs":[18,47,65],"often":[19,79],"rely":[20],"on":[21,99,179,198,207,219],"the":[22,31,38,52,74,97,103,127,140,146,153,180,185,199,208],"splitting":[23,32,111],"strategy":[24,33,129],"guided":[25],"by":[26,101],"meta-paths":[27,62,100],"or":[28],"relations.":[29],"However,":[30],"falls":[34],"short":[35],"in":[36],"capturing":[37],"rich":[39],"semantic":[40,54,141],"topological":[43],"structure":[44],"of":[45,110,135,145],"comprehensively,":[48],"as":[49,51,106,156],"well":[50],"hidden":[53],"associations":[55],"between":[56],"various":[57],"edge":[58,136],"types.":[59],"Moreover,":[60],"establishing":[61],"requires":[66],"substantial":[67],"expert":[68],"experience":[69],"domain":[71],"knowledge,":[72],"making":[73],"process":[75],"time-consuming,":[76],"laborious,":[77],"inaccurate.":[80],"To":[81],"address":[82],"these":[83],"challenges,":[84],"we":[85,124,151,191],"propose":[86],"a":[87,107,157,162],"novel":[88],"neural":[91],"network":[92],"called":[93],"RHGNN,":[94],"which":[95],"overcomes":[96],"reliance":[98],"whole":[108],"instead":[109],"it,":[112],"jointly":[114],"obtains":[115],"personalized":[116],"feature":[117,133],"representations":[118,134],"nodes":[120,171],"edges.":[122],"Specifically,":[123],"first":[125],"introduce":[126,192],"pre-training":[128],"Type2vec":[130],"to":[131,174,203],"learn":[132,175],"types":[137],"extract":[139],"structural":[143],"entire":[147],"graph.":[149],"Then":[150],"view":[152],"homogeneous":[158],"one,":[159],"design":[161],"new":[163],"propagation":[165],"aggregation":[167],"mechanism":[168],"both":[170,220],"edges":[173,183],"their":[176],"representations.":[177],"Based":[178],"assumption":[181],"that":[182,213],"with":[184],"same":[186],"type":[187],"have":[188],"similar":[189],"representations,":[190],"an":[193],"explicit":[194],"regularization":[195],"method":[196],"based":[197],"relational":[200],"inductive":[201],"bias":[202],"impose":[204],"smoothness":[205],"constraints":[206],"model.":[209],"Experimental":[210],"results":[211],"demonstrate":[212],"RHGNN":[214],"significantly":[215],"outperforms":[216],"state-of-the-art":[217],"models":[218],"node-level":[221],"edge-level":[223],"tasks.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}