乐胖代购免代理版

{"id":"https://openalex.org/W4396753645","doi":"https://doi.org/10.1109/access.2024.3398356","title":"CC-GNN: A clustering contrastive learning network for graph semi-supervised learning","display_name":"CC-GNN: A clustering contrastive learning network for graph semi-supervised learning","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4396753645","doi":"https://doi.org/10.1109/access.2024.3398356"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3398356","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10522632.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10522632.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103137738","display_name":"Peng Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Qin","raw_affiliation_strings":["Guangdong Province Key Laboratory, Sun Yat-sen University, Guangzhou, China","School of Mathematics, Sun-Yat Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Sun-Yat Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Guangdong Province Key Laboratory, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021656787","display_name":"Weifu Chen","orcid":"https://orcid.org/0000-0002-9375-2214"},"institutions":[{"id":"https://openalex.org/I4210129938","display_name":"Guangzhou Maritime College","ror":"https://ror.org/03kzcrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210129938"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weifu Chen","raw_affiliation_strings":["College of Information and Telecommunication Engineering, Guangzhou Maritime University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Information and Telecommunication Engineering, Guangzhou Maritime University, Guangzhou, China","institution_ids":["https://openalex.org/I4210129938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403018","display_name":"Min Zhang","orcid":"https://orcid.org/0009-0000-4185-940X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Zhang","raw_affiliation_strings":["School of Mathematics, Sun-Yat Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Sun-Yat Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101999863","display_name":"Defang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106134","display_name":"Guangzhou Vocational College of Science and Technology","ror":"https://ror.org/01dan7p53","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210106134"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Defang Li","raw_affiliation_strings":["Guangzhou Vocational College of Technology and Business, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangzhou Vocational College of Technology and Business, Guangzhou, China","institution_ids":["https://openalex.org/I4210106134"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090531329","display_name":"Guocan Feng","orcid":"https://orcid.org/0000-0002-0097-5591"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guocan Feng","raw_affiliation_strings":["Guangdong Province Key Laboratory, Sun Yat-sen University, Guangzhou, China","School of Mathematics, Sun-Yat Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Sun-Yat Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Guangdong Province Key Laboratory, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850,"provenance":"doaj"},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850,"provenance":"doaj"},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":84},"biblio":{"volume":"12","issue":null,"first_page":"71956","last_page":"71969"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9999,"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":0.9999,"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":0.9884,"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":0.9882,"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/clustering-coefficient","display_name":"Clustering coefficient","score":0.4986937},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature Learning","score":0.49438262},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.423252}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.72218007},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6435511},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6415498},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5653428},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.4986937},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.49438262},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43843484},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.423252},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3627774},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25547752},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3398356","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10522632.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3398356","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10522632.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"201804010255"}],"datasets":[],"versions":[],"referenced_works_count":34,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1662382123","https://openalex.org/W1977556410","https://openalex.org/W2016423476","https://openalex.org/W2132914434","https://openalex.org/W2153959628","https://openalex.org/W2158787690","https://openalex.org/W2558460151","https://openalex.org/W2768242641","https://openalex.org/W2788284887","https://openalex.org/W2883725317","https://openalex.org/W2962711740","https://openalex.org/W2964015378","https://openalex.org/W2966149470","https://openalex.org/W2966445777","https://openalex.org/W3012846134","https://openalex.org/W3022061250","https://openalex.org/W3033039844","https://openalex.org/W3035524453","https://openalex.org/W3087124270","https://openalex.org/W3173292968","https://openalex.org/W3176455777","https://openalex.org/W4200566466","https://openalex.org/W4212774754","https://openalex.org/W4214718285","https://openalex.org/W4226183968","https://openalex.org/W4285601999","https://openalex.org/W4287724327","https://openalex.org/W4287749054","https://openalex.org/W4287829537","https://openalex.org/W4297733535","https://openalex.org/W4300672471","https://openalex.org/W4304084091","https://openalex.org/W4362632778"],"related_works":["https://openalex.org/W4387497383","https://openalex.org/W4298178173","https://openalex.org/W4285218279","https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525"],"abstract_inverted_index":{"In":[0],"graph":[1,16,22,146,166,215],"modeling,":[2],"scarcity":[3],"of":[4,21,47,55,94,102,110,137,145,173,213],"labeled":[5],"data":[6,23,31,49,57,104],"is":[7,117],"a":[8,79,91,120,214],"challenging":[9],"issue.":[10],"To":[11,73],"address":[12],"this":[13,75,135],"issue,":[14],"state-of-the-art":[15],"models":[17,28,164,175,188],"learn":[18],"the":[19,45,53,62,70,100,108,155,171,187,206,211],"representation":[20],"via":[24],"contrastive":[25,39,96,115,138,157,208],"learning.":[26],"Those":[27],"usually":[29],"use":[30],"augmentation":[32],"techniques":[33],"to":[34,43,142,203],"generate":[35],"positive":[36,48,103],"pairs":[37,50],"for":[38,149,165,189],"learning,":[40],"which":[41,89,179],"aims":[42],"maximize":[44],"similarity":[46,54,101,109],"while":[51],"minimizing":[52],"negative":[56,111],"pairs.":[58],"However,":[59],"samples":[60],"with":[61,160],"same":[63],"labels":[64],"may":[65],"be":[66],"separately":[67],"mapped":[68],"in":[69],"feature":[71],"space.":[72],"solve":[74],"problem,":[76],"we":[77,198],"introduce":[78],"novel":[80],"model":[81],"called":[82],"Clustering":[83],"Contrastive":[84],"Graph":[85],"Neural":[86],"Network":[87],"(CC-GNN),":[88],"develops":[90],"new":[92],"kind":[93,136],"grouped":[95],"learning":[97,116,139,158,209],"that":[98,131,170],"maximizes":[99],"groups":[105],"and":[106],"minimizes":[107],"groups.":[112],"That":[113],"is,":[114],"defined":[118],"on":[119,125,192],"group":[121],"level":[122],"rather":[123],"than":[124],"an":[126,200],"instant":[127],"level.":[128],"We":[129,153,168,184],"assert":[130],"parameters":[132],"learned":[133],"by":[134],"will":[140],"lead":[141],"better":[143],"performance":[144,172,212],"neural":[147],"networks":[148],"downstream":[150],"classification":[151,191],"tasks.":[152],"combined":[154],"clustering":[156,207],"technique":[159],"three":[161,193],"baseline":[162],"GNN":[163],"classification.":[167],"found":[169],"these":[174],"was":[176],"significantly":[177],"improved,":[178],"strongly":[180],"supports":[181],"our":[182],"assertion.":[183],"also":[185],"testified":[186],"node":[190],"popular":[194],"citation":[195],"networks.":[196],"Finally,":[197],"conducted":[199],"ablation":[201],"study":[202],"analyze":[204],"how":[205],"influence":[210],"model.":[216]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4396753645","counts_by_year":[],"updated_date":"2024-12-24T02:35:41.643477","created_date":"2024-05-09"}