{"id":"https://openalex.org/W3201125152","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533429","title":"Ripple Walk Training: A Subgraph-based Training Framework for Large and Deep Graph Neural Network","display_name":"Ripple Walk Training: A Subgraph-based Training Framework for Large and Deep Graph Neural Network","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201125152","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533429","mag":"3201125152"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5103116134","display_name":"Jiyang Bai","orcid":"https://orcid.org/0000-0002-0621-8815"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiyang Bai","raw_affiliation_strings":["Florida State University, FL, USA"],"affiliations":[{"raw_affiliation_string":"Florida State University, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057157471","display_name":"Yuxiang Ren","orcid":"https://orcid.org/0000-0001-8829-3984"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuxiang Ren","raw_affiliation_strings":["Florida State University, FL, USA"],"affiliations":[{"raw_affiliation_string":"Florida State University, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100462845","display_name":"Jiawei Zhang","orcid":"https://orcid.org/0000-0002-8634-1687"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Zhang","raw_affiliation_strings":["Florida State University, FL, USA"],"affiliations":[{"raw_affiliation_string":"Florida State University, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103116134"],"corresponding_institution_ids":["https://openalex.org/I103163165"],"apc_list":null,"apc_paid":null,"fwci":2.4475,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.90916594,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"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/T12676","display_name":"Machine Learning and ELM","score":0.97079998254776,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9681000113487244,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7588193416595459},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5843809843063354},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4895549714565277},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.45908844470977783},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45605915784835815},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4264846444129944},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4185481369495392},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4084327518939972},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39079275727272034},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3836607038974762},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33237770199775696},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14868754148483276},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10840001702308655}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7588193416595459},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5843809843063354},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4895549714565277},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.45908844470977783},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45605915784835815},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4264846444129944},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4185481369495392},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4084327518939972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39079275727272034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3836607038974762},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33237770199775696},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14868754148483276},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10840001702308655},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.44999998807907104,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W637153065","https://openalex.org/W1514107797","https://openalex.org/W1522301498","https://openalex.org/W1662382123","https://openalex.org/W1665214252","https://openalex.org/W2048113368","https://openalex.org/W2116341502","https://openalex.org/W2125204570","https://openalex.org/W2139906443","https://openalex.org/W2153959628","https://openalex.org/W2166681504","https://openalex.org/W2558460151","https://openalex.org/W2751808960","https://openalex.org/W2759136286","https://openalex.org/W2786915849","https://openalex.org/W2790814121","https://openalex.org/W2804057010","https://openalex.org/W2809418595","https://openalex.org/W2890703109","https://openalex.org/W2945827377","https://openalex.org/W2949865801","https://openalex.org/W2952029609","https://openalex.org/W2961295589","https://openalex.org/W2962711740","https://openalex.org/W2962767366","https://openalex.org/W2963017945","https://openalex.org/W2963084622","https://openalex.org/W2963581908","https://openalex.org/W2963695795","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964114465","https://openalex.org/W2964121744","https://openalex.org/W2972693922","https://openalex.org/W2975868979","https://openalex.org/W2996268457","https://openalex.org/W2999269676","https://openalex.org/W3005916745","https://openalex.org/W3101553402","https://openalex.org/W3105136071","https://openalex.org/W4285723986","https://openalex.org/W4294558607","https://openalex.org/W6620673361","https://openalex.org/W6631190155","https://openalex.org/W6637178625","https://openalex.org/W6637242042","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6743410771","https://openalex.org/W6747954111","https://openalex.org/W6748141922","https://openalex.org/W6748799445","https://openalex.org/W6748848838","https://openalex.org/W6752110883","https://openalex.org/W6754030515","https://openalex.org/W6754929296","https://openalex.org/W6758068992","https://openalex.org/W6765543928","https://openalex.org/W6767077815","https://openalex.org/W6767998753","https://openalex.org/W6771932116","https://openalex.org/W6774110525"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3046775127","https://openalex.org/W3170094116","https://openalex.org/W4205958290","https://openalex.org/W4210836535"],"abstract_inverted_index":{"Graph":[0],"neural":[1,94],"networks":[2],"(GNNs)":[3],"have":[4],"achieved":[5],"outstanding":[6],"performance":[7],"in":[8,74,127,173,184],"learning":[9],"graph-structured":[10,157],"data":[11,44],"and":[12,37,58,65,91,107,153,169],"various":[13,175],"tasks.":[14],"However,":[15],"many":[16],"current":[17],"GNNs":[18,56,126,176],"suffer":[19],"from":[20,99],"three":[21],"common":[22],"problems":[23,40],"when":[24],"facing":[25],"large-size":[26],"graphs":[27,123,165],"or":[28,50],"using":[29],"a":[30,79,105,128],"deeper":[31],"structure:":[32],"neighbors":[33],"explosion,":[34],"node":[35],"dependence,":[36],"oversmoothing.":[38],"Such":[39],"attribute":[41],"to":[42,61,103,124,144],"the":[43,47,51,54,100,108,115,120,146,151,156,167,185],"structures":[45],"of":[46,53,155,164,171],"graph":[48,93,102],"itself":[49],"designing":[52],"multi-layers":[55],"framework,":[57,83],"can":[59],"lead":[60],"low":[62],"training":[63,82,174],"efficiency":[64,170],"high":[66],"space":[67],"complexity.":[68],"To":[69],"deal":[70],"with":[71],"these":[72,141],"problems,":[73],"this":[75],"paper,":[76],"we":[77],"propose":[78],"general":[80],"subgraph-based":[81],"namely":[84],"Ripple":[85,135],"Walk":[86,136],"Training":[87],"(RWT),":[88],"for":[89,139],"deep":[90],"large":[92],"networks.":[95],"RWT":[96,172],"samples":[97],"subgraphs":[98,143],"full":[101,109],"constitute":[104,145],"mini-batch,":[106,147],"GNN":[110],"is":[111,182],"updated":[112],"based":[113],"on":[114,161],"mini-batch":[116],"gradient.":[117],"We":[118],"analyze":[119],"high-quality":[121,142],"sub":[122],"train":[125],"theoretical":[129],"way.":[130],"A":[131],"novel":[132],"sampling":[133,140],"method":[134],"Sampler":[137],"works":[138],"which":[148],"considers":[149],"both":[150],"randomness":[152],"connectivity":[154],"data.":[158],"Extensive":[159],"experiments":[160],"different":[162],"sizes":[163],"demonstrate":[166],"effectiveness":[168],"(GCN":[177],"&":[178],"GAT).":[179],"Our":[180],"code":[181],"released":[183],"https://github.com/anonymous2review/RippleWalk.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}