{"id":"https://openalex.org/W4416251921","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228640","title":"Graph Local Pooling Optimization for Hierarchical Graph Neural Networks","display_name":"Graph Local Pooling Optimization for Hierarchical Graph Neural Networks","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251921","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228640"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228640","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5112963654","display_name":"Qinxin Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qinxin Zhao","raw_affiliation_strings":["Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060268538","display_name":"Sheng Zhong","orcid":"https://orcid.org/0000-0002-6581-8730"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Zhong","raw_affiliation_strings":["Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112963654"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1949817,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.9786999821662903,"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":0.9786999821662903,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.002099999925121665,"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/pooling","display_name":"Pooling","score":0.7224000096321106},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.5889999866485596},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.5019999742507935},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4839000105857849},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.44119998812675476},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.38600000739097595},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.38359999656677246}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7224000096321106},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6262000203132629},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.5889999866485596},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4674000144004822},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3828999996185303},{"id":"https://openalex.org/C17192189","wikidata":"https://www.wikidata.org/wiki/Q1347059","display_name":"Epigraph","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3021000027656555},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.27559998631477356},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228640","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1501856433","https://openalex.org/W1976320242","https://openalex.org/W2034618876","https://openalex.org/W2092750499","https://openalex.org/W2099438806","https://openalex.org/W2116341502","https://openalex.org/W2135957668","https://openalex.org/W2194775991","https://openalex.org/W2225892715","https://openalex.org/W2565330852","https://openalex.org/W2892880750","https://openalex.org/W2907101105","https://openalex.org/W2907492528","https://openalex.org/W2962810718","https://openalex.org/W2962818016","https://openalex.org/W3153321424","https://openalex.org/W3186542190","https://openalex.org/W4231449374","https://openalex.org/W4282926996","https://openalex.org/W4293363567","https://openalex.org/W4312281374","https://openalex.org/W4386083062","https://openalex.org/W4402704620"],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"there":[3],"has":[4],"been":[5],"significant":[6],"progress":[7],"in":[8,28,40,105,180],"extending":[9],"convolutional":[10],"neural":[11,132],"networks":[12],"to":[13,17,135,158,185],"graph":[14,22,30,90,121,131,156,214,218],"structures,":[15],"leading":[16],"the":[18,29,43,49,56,98,110,137,145,149,177,186,191,195,201,205,222],"development":[19],"of":[20,46,51,58,79,100,148,169,194,204],"various":[21],"convolution":[23,27,91],"operators":[24,64],"for":[25,67,140],"performing":[26],"domain.":[31],"Pooling,":[32],"another":[33],"essential":[34],"operation,":[35],"plays":[36],"a":[37,77,119],"crucial":[38],"role":[39],"progressively":[41],"reducing":[42],"spatial":[44],"dimensions":[45],"representations,":[47],"facilitating":[48],"creation":[50],"hierarchical":[52],"representations":[53],"and":[54],"minimizing":[55],"number":[57],"parameters.":[59],"However,":[60],"research":[61],"on":[62,75,82,217],"pooling":[63,123,215],"specifically":[65],"designed":[66],"graphs":[68,197],"remains":[69],"limited.":[70],"Some":[71],"existing":[72,213,234],"methods":[73],"focus":[74],"selecting":[76],"subset":[78],"nodes":[80,161],"based":[81],"scores":[83,139],"derived":[84,199],"from":[85,109,200],"trainable":[86],"projection":[87],"vectors":[88],"or":[89],"layers.":[92],"Unfortunately,":[93],"these":[94,170],"approaches":[95],"often":[96],"overlook":[97],"locality":[99],"nodes,":[101],"which":[102],"can":[103],"result":[104],"substantial":[106],"information":[107,147,203],"loss":[108],"original":[111,206],"graphs.":[112,166,207],"To":[113,143],"address":[114],"this":[115],"challenge,":[116],"we":[117,151],"introduce":[118],"learnable":[120],"local":[122],"method,":[124],"termed":[125],"LocalPool.":[126],"This":[127],"method":[128,211],"first":[129],"employs":[130],"network":[133,224],"operations":[134],"learn":[136],"importance":[138,188],"each":[141],"node.":[142],"preserve":[144],"structural":[146],"graph,":[150],"implement":[152],"an":[153],"efficient":[154],"vertex-weighted":[155],"cut":[157],"cluster":[159],"adjacent":[160],"into":[162],"supernodes":[163,171],"within":[164],"coarsened":[165,196],"The":[167,226],"features":[168,179],"are":[172],"then":[173],"generated":[174],"by":[175],"weighting":[176],"node":[178],"their":[181],"respective":[182],"clusters":[183],"according":[184],"learned":[187],"scores.":[189],"Additionally,":[190],"adjacency":[192],"matrix":[193],"is":[198],"clustering":[202],"We":[208],"evaluate":[209],"our":[210],"against":[212],"techniques":[216],"classification":[219],"benchmarks,":[220],"maintaining":[221],"same":[223],"architecture.":[225],"experimental":[227],"results":[228],"demonstrate":[229],"that":[230],"LocalPool":[231],"consistently":[232],"outperforms":[233],"methods.":[235]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
