{"id":"https://openalex.org/W4416016491","doi":"https://doi.org/10.1145/3746252.3761327","title":"GCLS <sup>2</sup> : Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics","display_name":"GCLS <sup>2</sup> : Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4416016491","doi":"https://doi.org/10.1145/3746252.3761327"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761327","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","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":null,"display_name":"Qi Wen","orcid":"https://orcid.org/0009-0001-0566-0505"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Wen","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0001-0566-0505","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054515750","display_name":"Y. S. Zhang","orcid":"https://orcid.org/0009-0005-8928-5392"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyang Zhang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0005-8928-5392","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102847794","display_name":"Yutong Ye","orcid":"https://orcid.org/0000-0002-6874-5741"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yutong Ye","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6874-5741","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040821130","display_name":"Yingbo Zhou","orcid":"https://orcid.org/0000-0001-6034-9667"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingbo Zhou","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6034-9667","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nan Zhang","orcid":"https://orcid.org/0009-0005-1392-8689"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Zhang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0005-1392-8689","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026993561","display_name":"Xiang Lian","orcid":"https://orcid.org/0000-0001-7681-3807"},"institutions":[{"id":"https://openalex.org/I149910238","display_name":"Kent State University","ror":"https://ror.org/049pfb863","country_code":"US","type":"education","lineage":["https://openalex.org/I149910238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Lian","raw_affiliation_strings":["Kent State University, Kent, OH, USA"],"raw_orcid":"https://orcid.org/0000-0001-7681-3807","affiliations":[{"raw_affiliation_string":"Kent State University, Kent, OH, USA","institution_ids":["https://openalex.org/I149910238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025413633","display_name":"Mingsong Chen","orcid":"https://orcid.org/0000-0002-3922-0989"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingsong Chen","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-3922-0989","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15555481,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3292","last_page":"3301"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9003000259399414,"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":0.9003000259399414,"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":0.07490000128746033,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/semantics","display_name":"Semantics (computer science)","score":0.6284999847412109},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5995000004768372},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5713000297546387},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48840001225471497},{"id":"https://openalex.org/keywords/community-structure","display_name":"Community structure","score":0.46470001339912415},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4359000027179718},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3628000020980835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6686000227928162},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6284999847412109},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5995000004768372},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5713000297546387},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48840001225471497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48420000076293945},{"id":"https://openalex.org/C133079900","wikidata":"https://www.wikidata.org/wiki/Q5155065","display_name":"Community structure","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44830000400543213},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4359000027179718},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36329999566078186},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3346000015735626},{"id":"https://openalex.org/C198942812","wikidata":"https://www.wikidata.org/wiki/Q496618","display_name":"Semantic property","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761327","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","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":26,"referenced_works":["https://openalex.org/W1970267553","https://openalex.org/W1971630691","https://openalex.org/W1973207991","https://openalex.org/W1995996823","https://openalex.org/W2002469984","https://openalex.org/W2006023152","https://openalex.org/W2031709923","https://openalex.org/W2068015060","https://openalex.org/W2070232376","https://openalex.org/W2098223336","https://openalex.org/W2122210493","https://openalex.org/W2125895010","https://openalex.org/W2290805605","https://openalex.org/W2745138925","https://openalex.org/W2889350994","https://openalex.org/W2963163921","https://openalex.org/W2986386290","https://openalex.org/W3035524453","https://openalex.org/W4283796272","https://openalex.org/W4313049505","https://openalex.org/W4360584675","https://openalex.org/W4382203390","https://openalex.org/W4382239090","https://openalex.org/W4399163935","https://openalex.org/W4401857145","https://openalex.org/W4403582565"],"related_works":[],"abstract_inverted_index":{"Due":[0],"to":[1,82,106,118,132,149],"the":[2,26,48,61,65,120,128,134,143,162,181,186,189,202,225],"power":[3],"of":[4,35,43,94,137,145,183,188,201,224,230],"learning":[5,12,32,77,99],"representations":[6,34],"from":[7,185],"unlabeled":[8],"graphs,":[9],"graph":[10,75,156,212],"contrastive":[11,76,98,130],"(GCL)":[13],"has":[14],"shown":[15],"excellent":[16],"performance":[17],"in":[18,47,56,222],"community":[19,27,50,62,66,104,163],"detection":[20,28,63,164],"tasks.":[21],"Existing":[22],"GCL-based":[23],"methods":[24],"on":[25,31,180,209],"usually":[29],"focused":[30],"attribute":[33],"individual":[36],"nodes,":[37,138],"which,":[38],"however,":[39],"ignores":[40],"structure":[41,67,79,114,129],"semantics":[42,68,80],"communities":[44,95],"(e.g.,":[45],"nodes":[46],"same":[49],"should":[51],"be":[52],"structurally":[53],"cohesive).":[54],"Therefore,":[55],"this":[57],"paper,":[58],"we":[59,101,126,152,175],"consider":[60],"under":[64,78],"and":[69,90,111,214,228],"propose":[70],"an":[71],"effective":[72],"framework":[73],"for":[74,166],"(GCLS2)":[81],"detect":[83],"communities.":[84,146,232],"To":[85,147],"seamlessly":[86],"integrate":[87],"interior":[88],"dense":[89],"exterior":[91],"sparse":[92],"characteristics":[93],"with":[96],"our":[97],"strategy,":[100],"employ":[102],"classic":[103],"structures":[105],"extract":[107],"high-level":[108,155],"structural":[109,122],"views":[110],"design":[112,153],"a":[113,154,177,197],"semantic":[115],"expression":[116],"module":[117],"augment":[119],"original":[121],"feature":[123,135],"representation.":[124],"Moreover,":[125],"formulate":[127],"loss":[131,165],"optimize":[133],"representation":[136,200],"which":[139],"can":[140,195],"better":[141],"capture":[142],"topology":[144],"adapt":[148],"large-scale":[150],"networks,":[151],"partitioning":[157],"(HGP)":[158],"algorithm":[159],"that":[160,174,216],"minimizes":[161],"GCLS2":[167,184,194,217],"online":[168],"training.":[169],"It":[170],"is":[171],"worth":[172],"noting":[173],"prove":[176],"lower":[178],"bound":[179],"training":[182],"perspective":[187],"information":[190],"theory,":[191],"explaining":[192],"why":[193],"learn":[196],"more":[198],"accurate":[199],"structure.":[203],"Extensive":[204],"experiments":[205],"have":[206],"been":[207],"conducted":[208],"various":[210],"real-world":[211],"datasets":[213],"confirmed":[215],"outperforms":[218],"nine":[219],"state-of-the-art":[220],"methods,":[221],"terms":[223],"accuracy,":[226],"modularity,":[227],"efficiency":[229],"detecting":[231]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}
