{"id":"https://openalex.org/W4286373861","doi":"https://doi.org/10.1109/tsp.2022.3192606","title":"Wide and Deep Graph Neural Network With Distributed Online Learning","display_name":"Wide and Deep Graph Neural Network With Distributed Online Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4286373861","doi":"https://doi.org/10.1109/tsp.2022.3192606"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2022.3192606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3192606","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2107.09203","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088013951","display_name":"Zhan Gao","orcid":"https://orcid.org/0000-0001-7250-7386"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhan Gao","raw_affiliation_strings":["Department of Computer Science Technology, University of Cambridge, Cambridge, U.K","Dept. of Electrical and Systems Eng., Univ. of Pennsylvania, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science Technology, University of Cambridge, Cambridge, U.K","institution_ids":[]},{"raw_affiliation_string":"Dept. of Electrical and Systems Eng., Univ. of Pennsylvania, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069506165","display_name":"Fernando Gama","orcid":"https://orcid.org/0000-0001-6117-8193"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fernando Gama","raw_affiliation_strings":["Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078862959","display_name":"Alejandro Ribeiro","orcid":"https://orcid.org/0000-0003-4230-9906"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alejandro Ribeiro","raw_affiliation_strings":["Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088013951"],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":3.4499,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.93369235,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"70","issue":null,"first_page":"3862","last_page":"3877"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9995999932289124,"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":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9912999868392944,"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/T12676","display_name":"Machine Learning and ELM","score":0.9912999868392944,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8172823190689087},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.606713056564331},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5834819674491882},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5576497912406921},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47859475016593933},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.470625638961792},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4395422637462616},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41808146238327026},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.37395554780960083}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8172823190689087},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.606713056564331},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5834819674491882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5576497912406921},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47859475016593933},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.470625638961792},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4395422637462616},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41808146238327026},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37395554780960083},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2022.3192606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3192606","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2107.09203","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2107.09203","pdf_url":"https://arxiv.org/pdf/2107.09203","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2107.09203","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2107.09203","pdf_url":"https://arxiv.org/pdf/2107.09203","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W607017180","https://openalex.org/W1561656882","https://openalex.org/W1964371315","https://openalex.org/W2044212084","https://openalex.org/W2069525910","https://openalex.org/W2104357911","https://openalex.org/W2116341502","https://openalex.org/W2117569556","https://openalex.org/W2123422798","https://openalex.org/W2143606444","https://openalex.org/W2219888463","https://openalex.org/W2471695703","https://openalex.org/W2475334473","https://openalex.org/W2792433162","https://openalex.org/W2796026362","https://openalex.org/W2796431263","https://openalex.org/W2798598284","https://openalex.org/W2885647718","https://openalex.org/W2943959761","https://openalex.org/W2963669306","https://openalex.org/W2971598376","https://openalex.org/W2984689574","https://openalex.org/W2993380743","https://openalex.org/W3039791234","https://openalex.org/W3104104151","https://openalex.org/W3123684968","https://openalex.org/W3130869292","https://openalex.org/W3136957854","https://openalex.org/W3160800226","https://openalex.org/W3161893952","https://openalex.org/W3166798139","https://openalex.org/W3175037169","https://openalex.org/W3176675329","https://openalex.org/W3209876175","https://openalex.org/W4230674625","https://openalex.org/W4292022450","https://openalex.org/W6631190155","https://openalex.org/W6638992375","https://openalex.org/W6675672627","https://openalex.org/W6680724558","https://openalex.org/W6720006811","https://openalex.org/W6754929296","https://openalex.org/W6756561102","https://openalex.org/W6779367026","https://openalex.org/W6796082418"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W2804383999","https://openalex.org/W2802049774"],"abstract_inverted_index":{"Graph":[0],"neural":[1],"networks":[2],"(GNNs)":[3],"are":[4,56,77],"naturally":[5],"distributed":[6,109,176,233],"architectures":[7],"for":[8,19,232],"learning":[9,59,111,178,210],"representations":[10,146],"from":[11,147],"network":[12],"data.":[13,148],"This":[14,92,165],"renders":[15],"them":[16],"suitable":[17],"candidates":[18],"decentralized":[20,186],"tasks.":[21],"In":[22],"these":[23],"scenarios,":[24],"the":[25,43,51,95,119,128,138,152,159,191,194,199,204,207,227,230],"underlying":[26,200],"graph":[27,125,201],"often":[28,166],"changes":[29,197],"with":[30,108],"time":[31,68],"due":[32],"to":[33,63,69,168,196],"link":[34],"failures":[35],"or":[36],"topology":[37],"variations,":[38],"creating":[39],"a":[40,101,123,132,169,175,185],"mismatch":[41],"between":[42],"graphs":[44],"on":[45,53,84,213],"which":[46,54,87],"GNNs":[47,65,88],"were":[48],"trained":[49],"and":[50,79,97,127,141,202,218,225],"ones":[52],"they":[55],"tested.":[57],"Online":[58],"can":[60,105,181],"be":[61,106,182],"leveraged":[62],"retrain":[64],"at":[66],"testing":[67,150],"overcome":[70],"this":[71],"issue.":[72],"However,":[73],"most":[74],"online":[75,110,177,209,234],"algorithms":[76],"centralized":[78],"usually":[80],"offer":[81],"guarantees":[82],"only":[83],"convex":[85,170],"problems,":[86],"rarely":[89],"lead":[90],"to.":[91],"paper":[93],"develops":[94],"Wide":[96],"Deep":[98],"GNN":[99],"(WD-GNN),":[100],"novel":[102],"architecture":[103,143],"that":[104,180],"updated":[107],"mechanisms.":[112],"The":[113],"WD-GNN":[114,195,231],"consists":[115],"of":[116,193,198,206,229],"two":[117],"components:":[118],"wide":[120,140],"part":[121,130,155],"is":[122,131,156],"linear":[124,154],"filter":[126],"deep":[129,142],"nonlinear":[133,145,161],"GNN.":[134],"At":[135,149],"training":[136],"time,":[137,151],"joint":[139],"learns":[144],"wide,":[153],"retrained,":[157],"while":[158],"deep,":[160],"one":[162],"remains":[163],"fixed.":[164],"leads":[167],"formulation.":[171],"We":[172,188],"further":[173],"propose":[174],"algorithm":[179],"implemented":[183],"in":[184],"setting.":[187],"also":[189],"show":[190,226],"stability":[192],"analyze":[203],"convergence":[205],"proposed":[208],"procedure.":[211],"Experiments":[212],"movie":[214],"recommendation,":[215],"source":[216],"localization":[217],"robot":[219],"swarm":[220],"control":[221],"corroborate":[222],"theoretical":[223],"findings":[224],"potential":[228],"learning.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
