{"id":"https://openalex.org/W4402352460","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650951","title":"PNAHN: Parallel Neighbor Aggregation for Heterogeneous Graph Neural Network","display_name":"PNAHN: Parallel Neighbor Aggregation for Heterogeneous Graph Neural Network","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352460","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650951"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650951","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5100739632","display_name":"Fan Li","orcid":"https://orcid.org/0000-0002-7763-2968"},"institutions":[{"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"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Li","raw_affiliation_strings":["University of Chinese Academy of Sciences,School of Engineering Science,Beijing,China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,School of Engineering Science,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107079592","display_name":"Kaihang Dou","orcid":null},"institutions":[{"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaihang Dou","raw_affiliation_strings":["University of Chinese Academy of Sciences,School of Mathematical Sciences,Beijing,China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,School of Mathematical Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053472681","display_name":"Suixiang Gao","orcid":null},"institutions":[{"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suixiang Gao","raw_affiliation_strings":["University of Chinese Academy of Sciences,Zhongguancun Laboratory,Beijing,China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Zhongguancun Laboratory,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100739632"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66505435,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"30","issue":null,"first_page":"1","last_page":"8"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9886000156402588,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9833999872207642,"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/computer-science","display_name":"Computer science","score":0.7618516683578491},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5521978139877319},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4792894721031189},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3456275165081024},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3336704969406128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7618516683578491},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5521978139877319},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4792894721031189},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3456275165081024},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3336704969406128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650951","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W1888005072","https://openalex.org/W2154851992","https://openalex.org/W2604314403","https://openalex.org/W2911286998","https://openalex.org/W2951626319","https://openalex.org/W2962756421","https://openalex.org/W2965857891","https://openalex.org/W2970066309","https://openalex.org/W2987294674","https://openalex.org/W2997461192","https://openalex.org/W3003795821","https://openalex.org/W3004507689","https://openalex.org/W3012871709","https://openalex.org/W3042085764","https://openalex.org/W3103513278","https://openalex.org/W3104097132","https://openalex.org/W3129758539","https://openalex.org/W3156968278","https://openalex.org/W3159075545","https://openalex.org/W3161072801","https://openalex.org/W3164473340","https://openalex.org/W3175971420","https://openalex.org/W3198399091","https://openalex.org/W3200804773","https://openalex.org/W4231449374","https://openalex.org/W4285378361","https://openalex.org/W4294558607","https://openalex.org/W4304811962","https://openalex.org/W4313423347","https://openalex.org/W4320854711","https://openalex.org/W4367047306","https://openalex.org/W4376171375","https://openalex.org/W4382202833","https://openalex.org/W4382317738","https://openalex.org/W4383469127","https://openalex.org/W4385245566","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6767098714"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Heterogeneous":[0,94],"graph":[1,122,172],"neural":[2],"networks":[3],"(HGNNs)":[4],"possess":[5],"powerful":[6],"representational":[7],"capabilities,":[8],"successfully":[9],"capturing":[10],"the":[11,26,45,53,57,63,70,82,141,162,178],"rich":[12],"semantic":[13,64,133],"and":[14,75,120,137,153],"structural":[15,138],"information":[16],"inherent":[17],"in":[18,37,56,129],"heterogeneous":[19,117,171],"graphs.":[20],"The":[21],"core":[22],"of":[23,28,40,104,151],"HGNNs":[24,33],"is":[25,77,126,158],"aggregation":[27,110,125],"neighbor":[29,105,109],"features.":[30],"However,":[31],"existing":[32,179],"often":[34],"lack":[35],"comprehensiveness":[36],"their":[38],"selection":[39],"neighbors.":[41],"They":[42],"either":[43],"overlook":[44],"intermediate":[46],"nodes":[47],"within":[48],"metapaths":[49],"as":[50,52],"well":[51],"direct":[54],"neighbors":[55,65],"original":[58,121,142],"graph,":[59],"or":[60],"they":[61],"neglect":[62],"connected":[66],"by":[67,107],"metapaths.":[68],"Furthermore,":[69],"potential":[71],"relationship":[72],"between":[73,148],"semantics":[74,152],"structure":[76],"rarely":[78],"considered.":[79],"To":[80],"address":[81],"limitations,":[83],"we":[84],"propose":[85],"a":[86,101,155],"novel":[87],"framework":[88],"named":[89],"Parallel":[90],"Neighbor":[91],"Aggregation":[92],"for":[93],"Graph":[95],"Neural":[96],"Network":[97],"(PNAHN).":[98],"PNAHN":[99,176],"utilizes":[100],"full":[102],"range":[103],"features":[106],"dividing":[108],"into":[111],"three":[112,169],"levels:":[113],"homogeneous":[114],"subgraph":[115,118],"level,":[116,119],"level.":[123],"Targeted":[124],"then":[127],"employed":[128],"parallel":[130],"to":[131,145,160],"derive":[132,161],"embedding":[134,139],"from":[135,140],"subgraphs":[136],"graph.":[143],"Finally,":[144],"comprehend":[146],"interactions":[147],"each":[149],"pair":[150],"structure,":[154],"transformer-based":[156],"fusion":[157],"applied":[159],"final":[163],"node":[164],"representations.":[165],"Empirical":[166],"experiments":[167],"on":[168],"real-world":[170],"datasets":[173],"demonstrate":[174],"that":[175],"outperforms":[177],"state-of-the-art":[180],"methods.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}