{"id":"https://openalex.org/W7131352940","doi":"https://doi.org/10.1007/s44196-025-01116-8","title":"Self-Supervised Heterogeneous Graph Neural Network with Multi-scale Meta-Path Contrastive Learning","display_name":"Self-Supervised Heterogeneous Graph Neural Network with Multi-scale Meta-Path Contrastive Learning","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131352940","doi":"https://doi.org/10.1007/s44196-025-01116-8"},"language":"en","primary_location":{"id":"doi:10.1007/s44196-025-01116-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-025-01116-8","pdf_url":null,"source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s44196-025-01116-8","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126763483","display_name":"Yufei Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161087","display_name":"Hebei University of Architecture","ror":"https://ror.org/058ange06","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210161087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufei Wu","raw_affiliation_strings":["Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China"],"affiliations":[{"raw_affiliation_string":"Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China","institution_ids":["https://openalex.org/I4210161087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126769363","display_name":"Xiumei Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210100300","display_name":"Zhangjiakou Academy of Agricultural Sciences","ror":"https://ror.org/00mpsrh50","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210100300"]},{"id":"https://openalex.org/I4210161087","display_name":"Hebei University of Architecture","ror":"https://ror.org/058ange06","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210161087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiumei Wen","raw_affiliation_strings":["Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China","Zhangjiakou Big Data Technology Innovation Center, Zhangjiakou , 075000, HebeiProvince, China"],"affiliations":[{"raw_affiliation_string":"Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China","institution_ids":["https://openalex.org/I4210161087"]},{"raw_affiliation_string":"Zhangjiakou Big Data Technology Innovation Center, Zhangjiakou , 075000, HebeiProvince, China","institution_ids":["https://openalex.org/I4210100300"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101801124","display_name":"Fanxing Meng","orcid":"https://orcid.org/0009-0007-8943-6802"},"institutions":[{"id":"https://openalex.org/I4210161087","display_name":"Hebei University of Architecture","ror":"https://ror.org/058ange06","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210161087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanxing Meng","raw_affiliation_strings":["Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China"],"affiliations":[{"raw_affiliation_string":"Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China","institution_ids":["https://openalex.org/I4210161087"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034424870","display_name":"Yingxue Mu","orcid":"https://orcid.org/0000-0001-9510-2402"},"institutions":[{"id":"https://openalex.org/I4210161087","display_name":"Hebei University of Architecture","ror":"https://ror.org/058ange06","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210161087"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingxue Mu","raw_affiliation_strings":["Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China"],"affiliations":[{"raw_affiliation_string":"Hebei University of Architecture, Zhangjiakou, 075000, Hebei Province, China","institution_ids":["https://openalex.org/I4210161087"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5034424870"],"corresponding_institution_ids":["https://openalex.org/I4210161087"],"apc_list":{"value":1390,"currency":"GBP","value_usd":1704},"apc_paid":{"value":1390,"currency":"GBP","value_usd":1704},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.63172553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9884999990463257,"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":0.9884999990463257,"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/T10028","display_name":"Topic Modeling","score":0.0010999999940395355,"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":0.0010999999940395355,"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/granularity","display_name":"Granularity","score":0.5990999937057495},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5615000128746033},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47380000352859497},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.44620001316070557},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4366999864578247},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4090999960899353},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3939000070095062}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8119000196456909},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5990999937057495},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5615000128746033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5271000266075134},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4860000014305115},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47380000352859497},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4366999864578247},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4090999960899353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3962000012397766},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.30390000343322754},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44196-025-01116-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-025-01116-8","pdf_url":null,"source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a6a0da5aba984061b178ef4e4f6461ba","is_oa":true,"landing_page_url":"https://doaj.org/article/a6a0da5aba984061b178ef4e4f6461ba","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Computational Intelligence Systems, Vol 19, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44196-025-01116-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-025-01116-8","pdf_url":null,"source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W2743104969","https://openalex.org/W2788919350","https://openalex.org/W2807021761","https://openalex.org/W2808927717","https://openalex.org/W2911286998","https://openalex.org/W2914721378","https://openalex.org/W2951626319","https://openalex.org/W2963707260","https://openalex.org/W2965857891","https://openalex.org/W2997686727","https://openalex.org/W3004507689","https://openalex.org/W3035524453","https://openalex.org/W3172710079","https://openalex.org/W3197400233","https://openalex.org/W4210707990","https://openalex.org/W4382203390","https://openalex.org/W4382239870","https://openalex.org/W4393372037"],"related_works":[],"abstract_inverted_index":{"Heterogeneous":[0,52],"graph":[1,53,123,216,233,269],"neural":[2,54],"networks":[3],"(HGNNs)":[4],"have":[5],"showcased":[6],"exceptional":[7],"modeling":[8,135],"prowess":[9],"in":[10,21,33,40,70,148,214,258],"characterizing":[11],"intricate":[12,137],"structures":[13,124],"and":[14,85,210,264],"diverse":[15],"semantic":[16,212],"information.":[17],"Despite":[18],"notable":[19],"advancements":[20],"capturing":[22],"high-order":[23],"relational":[24],"patterns":[25],"among":[26,179],"heterogeneous":[27,71,122,215,232,268],"nodes,":[28],"existing":[29],"methods":[30],"often":[31,152],"falter":[32],"accurately":[34],"expressing":[35],"local":[36,84,207],"neighborhood":[37],"information,":[38,88],"resulting":[39],"constraints":[41],"on":[42,229],"global\u2013local":[43],"structural":[44,87,168,208],"modeling.":[45],"To":[46,140,219],"address":[47,141],"this,":[48],"we":[49,107,225],"introduce":[50,104],"a":[51,62,76,109,157],"network":[55],"with":[56],"Multi-scale":[57],"Meta-path":[58],"Contrastive":[59],"learning":[60,69,113,270],"(HMMC),":[61],"self-supervised":[63,111],"framework":[64,114],"designed":[65],"to":[66,120],"enhance":[67],"representation":[68,99,177,217,261],"graphs.":[72],"Our":[73],"approach":[74],"incorporates":[75],"multi-scale":[77],"meta-path":[78],"embedding":[79],"mechanism":[80],"that":[81,115,238],"concurrently":[82],"captures":[83],"global":[86,211],"thereby":[89],"alleviating":[90],"the":[91,117,142,187,203,221,254],"challenge":[92],"where":[93],"overly":[94,101],"short":[95],"meta-paths":[96,103],"yield":[97],"limited":[98],"while":[100,182],"long":[102],"noise.":[105],"Furthermore,":[106],"devise":[108],"cross-view":[110],"contrastive":[112,127,150,159],"bolsters":[116],"model\u2019s":[118],"capacity":[119],"learn":[121],"by":[125,186],"executing":[126],"optimization":[128],"across":[129,248],"various":[130],"perspectives,":[131],"enabling":[132],"more":[133],"precise":[134],"of":[136,144,189,223,246,256,260],"node":[138,191],"relationships.":[139],"issue":[143],"noisy":[145],"negative":[146],"samples":[147],"traditional":[149],"learning\u2014which":[151],"hinders":[153],"model":[154],"optimization\u2014we":[155],"propose":[156],"star-shaped":[158],"loss":[160,164],"function.":[161],"This":[162],"inventive":[163],"function":[165],"exploits":[166],"center-neighborhood":[167],"dependencies":[169],"as":[170],"supervisory":[171],"signals":[172],"for":[173,267],"self-contrastive":[174],"learning,":[175],"ensuring":[176],"consistency":[178,213],"positive":[180],"pairs":[181],"circumventing":[183],"over-smoothing":[184],"caused":[185],"convergence":[188],"similar":[190],"embeddings.":[192],"By":[193],"integrating":[194],"these":[195],"two":[196],"highly":[197],"synergistic":[198],"modules,":[199],"our":[200],"method":[201],"resolves":[202],"inherent":[204],"trade-off":[205],"between":[206],"granularity":[209],"learning.":[218],"validate":[220],"effectiveness":[222],"HMMC,":[224],"conduct":[226],"comprehensive":[227],"experiments":[228],"multiple":[230],"public":[231],"datasets.":[234],"The":[235],"results":[236],"demonstrate":[237],"HMMC":[239,257],"surpasses":[240],"state-of-the-art":[241],"baselines,":[242],"achieving":[243],"performance":[244],"gains":[245],"0.5%\u20134.1%":[247],"different":[249],"benchmarks.":[250],"These":[251],"findings":[252],"substantiate":[253],"superiority":[255],"terms":[259],"power,":[262],"robustness,":[263],"generalization":[265],"capability":[266],"tasks.":[271]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2026-02-25T00:00:00"}