{"id":"https://openalex.org/W3091640079","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206735","title":"Higher-Order Heterogeneous Graph Convolutional Network Based on Meta-Paths","display_name":"Higher-Order Heterogeneous Graph Convolutional Network Based on Meta-Paths","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091640079","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206735","mag":"3091640079"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5102628364","display_name":"Wanting Zhao","orcid":null},"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":"Wanting Zhao","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101461718","display_name":"Hao Xu","orcid":"https://orcid.org/0000-0003-4207-6161"},"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":"Hao Xu","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013481647","display_name":"Wenzhuo Huang","orcid":"https://orcid.org/0000-0002-7797-508X"},"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":"Wenzhuo Huang","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083186948","display_name":"Jinkui Xie","orcid":null},"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":"Jinkui Xie","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102628364"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12115753,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","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":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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9980999827384949,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.974399983882904,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7060679197311401},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5682585835456848},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5400188565254211},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.5335251688957214},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5010716915130615},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.462732195854187},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.4613931179046631},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4360070526599884},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.27240246534347534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26337897777557373},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12291660904884338}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7060679197311401},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5682585835456848},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5400188565254211},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.5335251688957214},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5010716915130615},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.462732195854187},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.4613931179046631},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4360070526599884},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27240246534347534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26337897777557373},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12291660904884338},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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":49,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W1888005072","https://openalex.org/W2064675550","https://openalex.org/W2075010670","https://openalex.org/W2090891622","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2187089797","https://openalex.org/W2393319904","https://openalex.org/W2402144811","https://openalex.org/W2577283662","https://openalex.org/W2743104969","https://openalex.org/W2767774008","https://openalex.org/W2792485195","https://openalex.org/W2808856341","https://openalex.org/W2897133276","https://openalex.org/W2903634148","https://openalex.org/W2911286998","https://openalex.org/W2942681667","https://openalex.org/W2950898568","https://openalex.org/W2953384591","https://openalex.org/W2962756421","https://openalex.org/W2962767366","https://openalex.org/W2962904108","https://openalex.org/W2963169753","https://openalex.org/W2963707260","https://openalex.org/W2963858333","https://openalex.org/W2963893572","https://openalex.org/W2963919031","https://openalex.org/W2964015378","https://openalex.org/W2964565637","https://openalex.org/W2965857891","https://openalex.org/W2979086518","https://openalex.org/W2979220309","https://openalex.org/W3104097132","https://openalex.org/W4288363255","https://openalex.org/W4294170691","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W6682691769","https://openalex.org/W6690815549","https://openalex.org/W6713134421","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6745537798","https://openalex.org/W6746002097","https://openalex.org/W6746327351","https://openalex.org/W6755599798","https://openalex.org/W6758105487"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W2078477160","https://openalex.org/W1989103179","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2117632582","https://openalex.org/W4388347373"],"abstract_inverted_index":{"Graph":[0],"convolution":[1,79],"networks":[2],"are":[3,17],"potent":[4],"methods":[5],"in":[6,25,124,152],"graph":[7,37,78],"representation":[8],"learning.":[9],"Meta-paths,":[10],"which":[11],"connect":[12],"different":[13],"types":[14],"of":[15,69,108,142],"nodes,":[16],"extensively":[18],"used":[19],"to":[20,113],"represent":[21],"various":[22],"se-mantic":[23],"meanings":[24],"heterogeneous":[26,36,77,115,127],"graphs.":[27,116],"Inspired":[28],"by":[29],"the":[30,121,140],"above,":[31],"we":[32],"design":[33],"a":[34,47,66,75],"higher-order":[35,53,57,70,95,143],"convolutional":[38],"network":[39,80],"based":[40],"on":[41],"meta-paths.":[42],"It":[43],"not":[44],"only":[45],"chooses":[46],"few":[48],"meta-paths":[49,54],"but":[50],"also":[51],"captures":[52],"with":[55],"important":[56],"relations":[58],"(such":[59],"as":[60],"communal":[61],"relation).":[62],"Besides,":[63],"it":[64,90],"contains":[65],"calculation":[67,141],"method":[68],"meta-path-based":[71,96,144],"adjacency":[72,145],"matrices":[73,146],"and":[74,111,131,154],"novel":[76],"for":[81],"generating":[82],"node":[83],"embeddings.":[84],"At":[85],"every":[86],"message":[87],"passing":[88],"step,":[89],"linearly":[91],"aggregates":[92],"information":[93],"from":[94],"neighbors.":[97],"The":[98,135],"computational":[99],"complexity":[100],"analysis":[101],"shows":[102],"that":[103,139],"our":[104],"proposed":[105,118],"model":[106,119],"is":[107],"high":[109],"efficiency":[110],"applies":[112],"large-scale":[114],"Our":[117],"outperforms":[120],"state-of-the-art":[122],"results":[123],"three":[125],"real-world":[126],"graphs:":[128],"DBLP,":[129],"IMDB,":[130],"Amazon":[132],"Kindle":[133],"Review.":[134],"classification":[136],"experiments":[137],"show":[138],"brings":[147],"2.23%":[148],"average":[149],"accuracy":[150],"improvement":[151],"DBLP":[153],"IMDB.":[155]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}