{"id":"https://openalex.org/W4286909097","doi":"https://doi.org/10.1109/tnnls.2023.3243904","title":"Distributed Optimization of Graph Convolutional Network Using Subgraph Variance","display_name":"Distributed Optimization of Graph Convolutional Network Using Subgraph Variance","publication_year":2023,"publication_date":"2023-02-22","ids":{"openalex":"https://openalex.org/W4286909097","doi":"https://doi.org/10.1109/tnnls.2023.3243904","pmid":"https://pubmed.ncbi.nlm.nih.gov/37027692"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2023.3243904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3243904","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5034078186","display_name":"Taige Zhao","orcid":"https://orcid.org/0000-0001-8623-1878"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Taige Zhao","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0001-8623-1878","affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053270215","display_name":"Xiangyu Song","orcid":"https://orcid.org/0000-0002-5550-6354"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiangyu Song","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-5550-6354","affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404494","display_name":"Man Li","orcid":"https://orcid.org/0000-0002-7545-2541"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Man Li","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100380474","display_name":"Jianxin Li","orcid":"https://orcid.org/0000-0002-9059-330X"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jianxin Li","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-9059-330X","affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090419741","display_name":"Wei Luo","orcid":"https://orcid.org/0000-0002-4711-7543"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wei Luo","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-4711-7543","affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033585021","display_name":"Imran Razzak","orcid":"https://orcid.org/0000-0002-3930-6600"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Imran Razzak","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-3930-6600","affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149704539"],"apc_list":null,"apc_paid":null,"fwci":1.1784,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.81529164,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"35","issue":"8","first_page":"10764","last_page":"10775"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"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":0.9994999766349792,"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/T11478","display_name":"Caching and Content Delivery","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9785000085830688,"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/graph","display_name":"Graph","score":0.5835635662078857},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5649300813674927},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.44067540764808655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40366142988204956},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.3251456618309021},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3127768039703369},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25636953115463257}],"concepts":[{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5835635662078857},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5649300813674927},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.44067540764808655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40366142988204956},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.3251456618309021},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3127768039703369},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25636953115463257}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2023.3243904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3243904","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:37027692","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37027692","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3568341084","display_name":"Personalised Online Learning Analytics by Exploring Multilayer Graph Data ","funder_award_id":"LP180100750","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1932418350","https://openalex.org/W2004951603","https://openalex.org/W2022322548","https://openalex.org/W2070232376","https://openalex.org/W2104708700","https://openalex.org/W2116341502","https://openalex.org/W2309189658","https://openalex.org/W2555706138","https://openalex.org/W2807021761","https://openalex.org/W2907492528","https://openalex.org/W2945827377","https://openalex.org/W2958167848","https://openalex.org/W2970929262","https://openalex.org/W3080555959","https://openalex.org/W3086238199","https://openalex.org/W3088030941","https://openalex.org/W3095213891","https://openalex.org/W3100078588","https://openalex.org/W3100848837","https://openalex.org/W3101553402","https://openalex.org/W3112277895","https://openalex.org/W3117211396","https://openalex.org/W3126100018","https://openalex.org/W3136941290","https://openalex.org/W3138679477","https://openalex.org/W3159953606","https://openalex.org/W3162001942","https://openalex.org/W3188270315","https://openalex.org/W3197400233","https://openalex.org/W3209485964","https://openalex.org/W4210707990","https://openalex.org/W4288419263","https://openalex.org/W6695533872","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6748555532","https://openalex.org/W6748799445","https://openalex.org/W6748848838","https://openalex.org/W6754030515","https://openalex.org/W6760045743","https://openalex.org/W6765543928","https://openalex.org/W6776488958","https://openalex.org/W6781932242","https://openalex.org/W6785966533","https://openalex.org/W6786741872","https://openalex.org/W6790185353","https://openalex.org/W6793422453"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2980963558","https://openalex.org/W3161249280","https://openalex.org/W2267059662","https://openalex.org/W1882246857","https://openalex.org/W3103304230","https://openalex.org/W2185360082","https://openalex.org/W1992365905","https://openalex.org/W1976691234","https://openalex.org/W3212023493"],"abstract_inverted_index":{"In":[0,61],"recent":[1],"years,":[2],"distributed":[3,26,57,108,159,192],"graph":[4,39,55,76,84],"convolutional":[5],"networks":[6],"(GCNs)":[7],"training":[8,28,110,117],"frameworks":[9,29],"have":[10],"achieved":[11],"great":[12],"success":[13],"in":[14,200],"learning":[15],"the":[16,82,113,116,144,150,175,183,212],"representation":[17],"of":[18,37,99,115,146,152,191],"graph-structured":[19],"data":[20,40],"with":[21],"large":[22],"sizes.":[23],"However,":[24],"existing":[25],"GCN":[27,58,109,160,193],"require":[30],"enormous":[31],"communication":[32,90,176],"costs":[33],"since":[34],"a":[35,54,121,129,197],"multitude":[36],"dependent":[38],"need":[41],"to":[42,88,137,148,211],"be":[43],"transmitted":[44],"from":[45],"other":[46,100],"processors.":[47],"To":[48,104],"address":[49],"this":[50],"issue,":[51],"we":[52,119],"propose":[53,73,128],"augmentation-based":[56,75],"framework":[59,172],"(GAD).":[60],"particular,":[62],"GAD":[63],"has":[64],"two":[65],"main":[66],"components:":[67],"GAD-Partition":[68,157],"and":[69,93,111,127,195],"GAD-Optimizer.":[70,139],"We":[71],"first":[72],"an":[74],"partition":[77],"(GAD-Partition)":[78],"that":[79,170],"can":[80],"divide":[81],"input":[83],"into":[85],"augmented":[86],"subgraphs":[87,147],"reduce":[89,149],"by":[91,156],"selecting":[92],"storing":[94],"as":[95,102,138],"few":[96],"significant":[97],"vertices":[98],"processors":[101],"possible.":[103],"further":[105],"speed":[106,185],"up":[107],"improve":[112],"quality":[114],"result,":[118],"design":[120],"subgraph":[122],"variance-based":[123],"importance":[124,145],"calculation":[125],"formula":[126],"novel":[130],"weighted":[131],"global":[132],"consensus":[133],"method,":[134],"collectively":[135],"referred":[136],"This":[140],"optimizer":[141],"adaptively":[142],"adjusts":[143],"effect":[151],"extra":[153],"variance":[154],"introduced":[155],"on":[158,164,207],"training.":[161],"Extensive":[162],"experiments":[163],"four":[165],"large-scale":[166],"real-world":[167],"datasets":[168],"demonstrate":[169],"our":[171],"significantly":[173],"reduces":[174],"overhead":[177],"(":[178,186,202],"\u2248":[179,187,203],"50%":[180],"),":[181],"improves":[182],"convergence":[184],"2":[188],"\u00d7":[189],")":[190,205],"training,":[194],"obtains":[196],"slight":[198],"gain":[199],"accuracy":[201],"0.45%":[204],"based":[206],"minimal":[208],"redundancy":[209],"compared":[210],"state-of-the-art":[213],"methods.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
