{"id":"https://openalex.org/W2993822271","doi":"https://doi.org/10.1109/access.2019.2955487","title":"Neighborhood Adaptive Graph Convolutional Network for Node Classification","display_name":"Neighborhood Adaptive Graph Convolutional Network for Node Classification","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2993822271","doi":"https://doi.org/10.1109/access.2019.2955487","mag":"2993822271"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2955487","doi":"https://doi.org/10.1109/access.2019.2955487","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2955487","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08911311.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","oarl","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W2993822271"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08911311.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082357985","display_name":"Peiliang Gong","orcid":"https://orcid.org/0000-0003-2611-3145","observed_orcids":["https://orcid.org/0000-0003-2611-3145"]},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiliang Gong","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2611-3145","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089830603","display_name":"Lihua Ai","orcid":"https://orcid.org/0000-0001-5864-2186","observed_orcids":["https://orcid.org/0000-0001-5864-2186"]},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihua Ai","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5864-2186","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":{"value":1750,"currency":"USD","value_usd":1750},"apc_paid":{"value":1750,"currency":"USD","value_usd":1750},"fwci":0.7012,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.73196098,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"170578","last_page":"170588"},"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":0.0,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.0,"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/graph-convolutional-networks","display_name":"graph convolutional networks","score":0.8080000281333923},{"id":"https://openalex.org/keywords/node-classification","display_name":"node classification","score":0.9779999852180481},{"id":"https://openalex.org/keywords/diffusion","display_name":"diffusion","score":0.5659999847412109},{"id":"https://openalex.org/keywords/feature-refinement","display_name":"feature refinement","score":0.7990000247955322},{"id":"https://openalex.org/keywords/spectral-convolution","display_name":"spectral convolution","score":0.8059999942779541},{"id":"https://openalex.org/keywords/message-passing","display_name":"message passing","score":0.9900000095367432},{"id":"https://openalex.org/keywords/node-representation-learning","display_name":"node representation learning","score":0.6669999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7763789892196655},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5935207009315491},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5255752205848694},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5214749574661255},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.48398661613464355},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44699418544769287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3314320147037506},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14617088437080383},{"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2955487","doi":"https://doi.org/10.1109/access.2019.2955487","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2955487","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08911311.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","oarl","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:91e2c56cfb82480d8c1855e1a635cfb2","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/91e2c56cfb82480d8c1855e1a635cfb2","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 170578-170588 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2955487","doi":"https://doi.org/10.1109/access.2019.2955487","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2955487","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08911311.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","oarl","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4017326635","display_name":null,"funder_award_id":"351409535","funder_id":"https://openalex.org/F4320323066","funder_display_name":"Beijing Jiaotong University"},{"id":"https://openalex.org/G462822677","display_name":null,"funder_award_id":"145028522","funder_id":"https://openalex.org/F4320323066","funder_display_name":"Beijing Jiaotong University"}],"funders":[{"id":"https://openalex.org/F4320323066","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2993822271.pdf","grobid_xml":"https://content.openalex.org/works/W2993822271.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W67413104","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1662382123","https://openalex.org/W1888005072","https://openalex.org/W1908728294","https://openalex.org/W1951806617","https://openalex.org/W2095705004","https://openalex.org/W2096091969","https://openalex.org/W2104290444","https://openalex.org/W2139823104","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2159291644","https://openalex.org/W2162630660","https://openalex.org/W2168190036","https://openalex.org/W2187089797","https://openalex.org/W2315403234","https://openalex.org/W2407712691","https://openalex.org/W2468907370","https://openalex.org/W2519887557","https://openalex.org/W2558460151","https://openalex.org/W2606202972","https://openalex.org/W2606780347","https://openalex.org/W2624431344","https://openalex.org/W2792839479","https://openalex.org/W2798638375","https://openalex.org/W2891649471","https://openalex.org/W2902558981","https://openalex.org/W2907620438","https://openalex.org/W2916798096","https://openalex.org/W2947391323","https://openalex.org/W2962756421","https://openalex.org/W2963403664","https://openalex.org/W2963782635","https://openalex.org/W4239931746","https://openalex.org/W4285723986","https://openalex.org/W7099972890"],"related_works":["https://openalex.org/W4396238973","https://openalex.org/W4220937924","https://openalex.org/W4394966790","https://openalex.org/W4313010772","https://openalex.org/W7084041659","https://openalex.org/W7108324235","https://openalex.org/W2902705165","https://openalex.org/W7136083644","https://openalex.org/W6928806839"],"abstract_inverted_index":{"Recently,":[0],"graph":[1,25,54],"convolutional":[2,55,71],"neural":[3],"network":[4,56],"as":[5,79],"an":[6],"efficient":[7],"and":[8,15,88,112,175],"effective":[9],"method":[10,20,60,102,185],"has":[11],"experienced":[12],"significant":[13],"attention":[14],"becomes":[16],"the":[17,44,75,80,109,117,127,133,142,153],"de":[18],"facto":[19],"for":[21,40,94,138],"learning":[22],"node":[23,38,41,92,140,158,191],"or":[24],"representations.":[26,66],"However,":[27],"existing":[28],"most":[29,134],"methods":[30],"use":[31],"a":[32,51,58,70,98,123],"fixed-order":[33],"neighborhood":[34,52,81,91],"information":[35,93,107],"when":[36],"integrating":[37],"representations":[39,159],"classification":[42,192],"on":[43,126,141,190],"graph.":[45,143],"In":[46],"this":[47],"paper,":[48],"we":[49,68],"present":[50],"adaptive":[53,82],"(NAGCN),":[57],"novel":[59],"to":[61,84,116,130,155,195],"efficiently":[62],"learn":[63,87,104],"each":[64,95,139],"node's":[65],"Particularly,":[67],"construct":[69],"kernel":[72,83,129],"abstracted":[73],"from":[74],"diffusion":[76],"process,":[77],"named":[78],"more":[85,105],"precisely":[86],"integrate":[89],"related":[90,197],"node.":[96],"As":[97],"result,":[99],"our":[100,184],"proposed":[101],"can":[103,186],"useful":[106],"across":[108],"relevant":[110],"near":[111],"distant":[113],"neighbors":[114],"according":[115],"real":[118],"applications.":[119],"We":[120],"also":[121,167],"adopt":[122],"threshold":[124],"mechanism":[125],"constructed":[128],"better":[131,188],"reserve":[132],"impact":[135],"neighbor":[136],"vertices":[137],"Besides,":[144],"one":[145],"learnable":[146],"feature":[147],"refinement":[148],"process":[149],"is":[150,166],"used":[151],"in":[152,170],"model":[154,165],"obtain":[156],"high-level":[157],"with":[160],"sufficient":[161],"expressive":[162],"power.":[163],"The":[164],"theoretically":[168],"analyzed":[169],"terms":[171],"of":[172],"spectral":[173],"convolution":[174],"message":[176],"passing":[177],"algorithm.":[178],"Notably,":[179],"extensive":[180],"experiments":[181],"demonstrate":[182],"that":[183],"achieve":[187],"performance":[189],"tasks":[193],"compared":[194],"other":[196],"approaches.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2019-12-13T00:00:00"}
