{"id":"https://openalex.org/W3217672792","doi":"https://doi.org/10.1145/3490478","title":"Combining Graph Convolutional Neural Networks and Label Propagation","display_name":"Combining Graph Convolutional Neural Networks and Label Propagation","publication_year":2021,"publication_date":"2021-11-29","ids":{"openalex":"https://openalex.org/W3217672792","doi":"https://doi.org/10.1145/3490478","mag":"3217672792"},"language":"en","primary_location":{"id":"doi:10.1145/3490478","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3490478","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3490478","source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3490478","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100357128","display_name":"Hongwei Wang","orcid":"https://orcid.org/0000-0001-7474-8271"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongwei Wang","raw_affiliation_strings":["Stanford University, CA, United States"],"raw_orcid":"https://orcid.org/0000-0001-7474-8271","affiliations":[{"raw_affiliation_string":"Stanford University, CA, United States","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091272738","display_name":"Jure Leskovec","orcid":"https://orcid.org/0000-0002-5411-923X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jure Leskovec","raw_affiliation_strings":["Stanford University, CA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, CA, United States","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":6.6877,"has_fulltext":true,"cited_by_count":72,"citation_normalized_percentile":{"value":0.97277559,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"40","issue":"4","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9955999851226807,"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/computer-science","display_name":"Computer science","score":0.7749335169792175},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6100887060165405},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5374596118927002},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5161396265029907},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5014848709106445},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.49147212505340576},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4828513264656067},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4171265661716461},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3938257098197937},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36420297622680664},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3378691077232361}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7749335169792175},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6100887060165405},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5374596118927002},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5161396265029907},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5014848709106445},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.49147212505340576},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4828513264656067},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4171265661716461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3938257098197937},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36420297622680664},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3378691077232361},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3490478","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3490478","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3490478","source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3490478","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3490478","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3490478","source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.47999998927116394}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320310366","display_name":"Hitachi","ror":"https://ror.org/02exqgm79"},{"id":"https://openalex.org/F4320314870","display_name":"Dell Technologies","ror":null},{"id":"https://openalex.org/F4320316375","display_name":"Visa","ror":"https://ror.org/05t1y0b59"},{"id":"https://openalex.org/F4320316620","display_name":"Amazon Catalyst","ror":"https://ror.org/04mv4n011"},{"id":"https://openalex.org/F4320318127","display_name":"UnitedHealth Group","ror":"https://ror.org/04a8rt780"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320333615","display_name":"Wu Tsai Neurosciences Institute, Stanford University","ror":null},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3217672792.pdf","grobid_xml":"https://content.openalex.org/works/W3217672792.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1969198379","https://openalex.org/W1994389483","https://openalex.org/W2010187764","https://openalex.org/W2053186076","https://openalex.org/W2094286023","https://openalex.org/W2153959628","https://openalex.org/W2323738766","https://openalex.org/W2509893387","https://openalex.org/W2543728464","https://openalex.org/W2602856279","https://openalex.org/W2604738573","https://openalex.org/W2787740662","https://openalex.org/W2792839191","https://openalex.org/W2792839479","https://openalex.org/W2913668833","https://openalex.org/W2948729509","https://openalex.org/W2963241951","https://openalex.org/W2963415211","https://openalex.org/W2963911286","https://openalex.org/W2979683452","https://openalex.org/W3084428871","https://openalex.org/W3098087397","https://openalex.org/W3129482887","https://openalex.org/W3155919942","https://openalex.org/W3156441686","https://openalex.org/W3187966659","https://openalex.org/W6682839888"],"related_works":["https://openalex.org/W2767651786","https://openalex.org/W2912288872","https://openalex.org/W564581980","https://openalex.org/W3106036237","https://openalex.org/W2886673456","https://openalex.org/W2295021132","https://openalex.org/W2546942002","https://openalex.org/W2970216048","https://openalex.org/W785854688","https://openalex.org/W2382607599"],"abstract_inverted_index":{"Label":[0],"Propagation":[1],"Algorithm":[2],"(LPA)":[3],"and":[4,39,52,66,86,128,138,178,198],"Graph":[5],"Convolutional":[6],"Neural":[7],"Networks":[8],"(GCN)":[9],"are":[10,136],"both":[11],"message":[12],"passing":[13],"algorithms":[14],"on":[15,116,171],"graphs.":[16],"Both":[17],"solve":[18],"the":[19,31,34,77,82,100,107,139,146,166],"task":[20],"of":[21,33,90,103,110,168,192],"node":[22,27,41,105,112,172,196],"classification,":[23],"but":[24],"LPA":[25,51,65,85,140],"propagates":[26,38],"label":[28],"information":[29],"across":[30],"edges":[32,169],"graph,":[35],"while":[36,45],"GCN":[37,53,67,87,127,147],"transforms":[40],"feature":[42],"information.":[43],"However,":[44],"conceptually":[46],"similar,":[47],"theoretical":[48,118],"relationship":[49,83],"between":[50,84],"has":[54],"not":[55],"yet":[56],"been":[57],"systematically":[58],"investigated.":[59],"Moreover,":[60],"it":[61],"is":[62,175],"unclear":[63],"how":[64,98],"can":[68,160],"be":[69,162],"combined":[70],"under":[71],"a":[72,190],"unified":[73,132],"framework":[74],"to":[75,144,155],"improve":[76],"performance.":[78,157],"Here":[79],"we":[80,96,120],"study":[81],"in":[88,94,113,148],"terms":[89],"feature/label":[91,102,109],"influence":[92],",":[93],"which":[95,174],"characterize":[97],"much":[99],"initial":[101],"one":[104],"influences":[106],"final":[108],"another":[111],"GCN/LPA.":[114],"Based":[115],"our":[117,131,201],"analysis,":[119],"propose":[121],"an":[122],"end-to-end":[123],"model":[124,159,202],"that":[125,153],"combines":[126],"LPA.":[129],"In":[130,189],"model,":[133],"edge":[134,151],"weights":[135,152,167],"learnable,":[137],"serves":[141],"as":[142,164],"regularization":[143],"assist":[145],"learning":[149,165],"proper":[150],"lead":[154],"improved":[156],"Our":[158],"also":[161],"seen":[163],"based":[170],"labels,":[173],"more":[176],"direct":[177],"efficient":[179],"than":[180],"existing":[181],"feature-based":[182],"attention":[183],"models":[184],"or":[185],"topology-based":[186],"diffusion":[187],"models.":[188],"number":[191],"experiments":[193],"for":[194],"semi-supervised":[195],"classification":[197],"knowledge-graph-aware":[199],"recommendation,":[200],"shows":[203],"superiority":[204],"over":[205],"state-of-the-art":[206],"baselines.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":11}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}