{"id":"https://openalex.org/W4387846787","doi":"https://doi.org/10.1145/3583780.3614778","title":"AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange","display_name":"AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846787","doi":"https://doi.org/10.1145/3583780.3614778"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3614778","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614778","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614778","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614778","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067975913","display_name":"Liang Zeng","orcid":"https://orcid.org/0000-0003-2295-2996"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liang Zeng","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103012751","display_name":"Jin Xu","orcid":"https://orcid.org/0000-0002-1409-6731"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Xu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046687207","display_name":"Zijun Yao","orcid":"https://orcid.org/0000-0002-0288-9283"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijun Yao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015101640","display_name":"Yanqiao Zhu","orcid":"https://orcid.org/0000-0003-2205-5304"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanqiao Zhu","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101938872","display_name":"Jian Li","orcid":"https://orcid.org/0000-0003-4650-3925"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5067975913"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.5147,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72253623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3134","last_page":"3143"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9941999912261963,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9695000052452087,"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":0.8224718570709229},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6118780374526978},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5554013252258301},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5101697444915771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46715039014816284},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33086317777633667},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2790085971355438}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8224718570709229},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6118780374526978},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5554013252258301},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5101697444915771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46715039014816284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33086317777633667},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2790085971355438},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3614778","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614778","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614778","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3583780.3614778","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614778","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614778","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G37568934","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387846787.pdf","grobid_xml":"https://content.openalex.org/works/W4387846787.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W2107559689","https://openalex.org/W2783961799","https://openalex.org/W2807021761","https://openalex.org/W2943160321","https://openalex.org/W3034235489","https://openalex.org/W3034903580","https://openalex.org/W3036446966","https://openalex.org/W3039075121","https://openalex.org/W3042770487","https://openalex.org/W3082034060","https://openalex.org/W3095746859","https://openalex.org/W3099152386","https://openalex.org/W3099375322","https://openalex.org/W3100848837","https://openalex.org/W3100993589","https://openalex.org/W3154503084","https://openalex.org/W3160872503","https://openalex.org/W3212515727","https://openalex.org/W4206482253","https://openalex.org/W4235571646","https://openalex.org/W4310895557","https://openalex.org/W6601955380"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"already":[5],"been":[6],"widely":[7],"used":[8],"in":[9,23,124,138],"various":[10,158],"graph":[11,59,97,101,112,171,192],"mining":[12],"tasks.":[13],"However,":[14],"recent":[15],"works":[16],"reveal":[17],"that":[18,196],"the":[19,32,52,90,125,218],"learned":[20],"weights":[21],"(channels)":[22],"well-trained":[24],"GNNs":[25,56,107,144],"are":[26],"highly":[27],"redundant,":[28],"which":[29,88],"inevitably":[30],"limits":[31],"performance":[33,155],"of":[34,37,55,129,135,163,179,220],"GNNs.":[35],"Instead":[36],"removing":[38],"these":[39,68],"redundant":[40,69,122],"channels":[41,70,74,123,134],"for":[42,57],"efficiency":[43],"consideration,":[44],"we":[45,64,175],"aim":[46],"to":[47,50,66,75,110,114],"reactivate":[48],"them":[49],"enlarge":[51],"representation":[53],"capacity":[54],"effective":[58],"learning.":[60],"In":[61,173],"this":[62,77],"paper,":[63],"propose":[65],"substitute":[67],"with":[71,132,157],"other":[72],"informative":[73,116,133],"achieve":[76],"goal.":[78],"We":[79],"introduce":[80],"a":[81,139,161,177],"novel":[82],"GNN":[83,131,137,188,202],"learning":[84],"framework":[85],"named":[86],"AKE-GNN,":[87],"performs":[89],"Adaptive":[91],"Knowledge":[92],"Exchange":[93],"strategy":[94],"among":[95],"multiple":[96,106],"views":[98],"generated":[99],"by":[100],"augmentations.":[102],"AKE-GNN":[103,119,152,197],"first":[104],"trains":[105],"each":[108],"corresponding":[109],"one":[111,130],"view":[113],"obtain":[115],"channels.":[117],"Then,":[118],"iteratively":[120],"exchanges":[121],"weight":[126],"parameter":[127],"matrix":[128],"another":[136],"layer-wise":[140],"manner.":[141],"Additionally,":[142],"existing":[143,200],"can":[145],"be":[146],"seamlessly":[147],"incorporated":[148],"into":[149],"our":[150],"framework.":[151],"achieves":[153],"superior":[154],"compared":[156],"baselines":[159],"across":[160],"suite":[162],"experiments":[164,180],"on":[165,181,213],"node":[166],"classification,":[167],"link":[168],"prediction,":[169],"and":[170,190,194,204,211],"classification.":[172],"particular,":[174],"conduct":[176],"series":[178],"15":[182],"public":[183],"benchmark":[184],"datasets,":[185],"8":[186],"popular":[187,201],"models,":[189],"3":[191],"tasks":[193],"show":[195],"consistently":[198],"outperforms":[199],"models":[203],"even":[205],"their":[206],"ensembles.":[207],"Extensive":[208],"ablation":[209],"studies":[210],"analyses":[212],"knowledge":[214],"exchange":[215],"methods":[216],"validate":[217],"effectiveness":[219],"AKE-GNN.":[221]},"counts_by_year":[{"year":2024,"cited_by_count":3}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
