{"id":"https://openalex.org/W3136263832","doi":"https://doi.org/10.1109/bigdata50022.2020.9378123","title":"Community Detection using Semi-supervised Learning with Graph Convolutional Network on GPUs","display_name":"Community Detection using Semi-supervised Learning with Graph Convolutional Network on GPUs","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3136263832","doi":"https://doi.org/10.1109/bigdata50022.2020.9378123","mag":"3136263832"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5078146729","display_name":"Naw Safrin Sattar","orcid":"https://orcid.org/0000-0002-6199-346X"},"institutions":[{"id":"https://openalex.org/I192396691","display_name":"University of New Orleans","ror":"https://ror.org/034mtvk83","country_code":"US","type":"education","lineage":["https://openalex.org/I192396691","https://openalex.org/I2799628689"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Naw Safrin Sattar","raw_affiliation_strings":["Department of Computer Science, University of New Orleans, New Orleans, LA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of New Orleans, New Orleans, LA, USA","institution_ids":["https://openalex.org/I192396691"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033369839","display_name":"Shaikh Arifuzzaman","orcid":"https://orcid.org/0000-0002-6893-2475"},"institutions":[{"id":"https://openalex.org/I192396691","display_name":"University of New Orleans","ror":"https://ror.org/034mtvk83","country_code":"US","type":"education","lineage":["https://openalex.org/I192396691","https://openalex.org/I2799628689"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaikh Arifuzzaman","raw_affiliation_strings":["Department of Computer Science, University of New Orleans, New Orleans, LA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of New Orleans, New Orleans, LA, USA","institution_ids":["https://openalex.org/I192396691"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078146729"],"corresponding_institution_ids":["https://openalex.org/I192396691"],"apc_list":null,"apc_paid":null,"fwci":0.7966,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.71878521,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5237","last_page":"5246"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9965000152587891,"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/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9793000221252441,"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/computer-science","display_name":"Computer science","score":0.8642449378967285},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7902536392211914},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6235454082489014},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.47379085421562195},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4679471552371979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44090601801872253},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3796079158782959},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3217007517814636},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.21602383255958557},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10845455527305603}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8642449378967285},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7902536392211914},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6235454082489014},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.47379085421562195},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4679471552371979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44090601801872253},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3796079158782959},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3217007517814636},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.21602383255958557},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10845455527305603}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309392","display_name":"Louisiana Board of Regents","ror":"https://ror.org/00jv89z46"},{"id":"https://openalex.org/F4320311490","display_name":"University of New Orleans","ror":"https://ror.org/034mtvk83"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W106504814","https://openalex.org/W1728842521","https://openalex.org/W1971421925","https://openalex.org/W1989788024","https://openalex.org/W1995996823","https://openalex.org/W2047940964","https://openalex.org/W2101234009","https://openalex.org/W2109726592","https://openalex.org/W2125910575","https://openalex.org/W2131681506","https://openalex.org/W2133990480","https://openalex.org/W2294347342","https://openalex.org/W2519887557","https://openalex.org/W2751808960","https://openalex.org/W2755088640","https://openalex.org/W2765108993","https://openalex.org/W2770468159","https://openalex.org/W2886462128","https://openalex.org/W2898221481","https://openalex.org/W2899048350","https://openalex.org/W2899771611","https://openalex.org/W2914500685","https://openalex.org/W2922080080","https://openalex.org/W2935683358","https://openalex.org/W2945827377","https://openalex.org/W2948713006","https://openalex.org/W2961295589","https://openalex.org/W2962767366","https://openalex.org/W2962946486","https://openalex.org/W2963486920","https://openalex.org/W2964015378","https://openalex.org/W2964198013","https://openalex.org/W2964236544","https://openalex.org/W2970090796","https://openalex.org/W2994968268","https://openalex.org/W3004718539","https://openalex.org/W3008556495","https://openalex.org/W3046920626","https://openalex.org/W3099768174","https://openalex.org/W3100078588","https://openalex.org/W3101553402","https://openalex.org/W3124834154","https://openalex.org/W4288102469","https://openalex.org/W4293412117","https://openalex.org/W4294558607","https://openalex.org/W6637572315","https://openalex.org/W6697195335","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6743410771","https://openalex.org/W6744271739","https://openalex.org/W6745933383","https://openalex.org/W6746260573","https://openalex.org/W6756040250","https://openalex.org/W6756248755","https://openalex.org/W6760423706","https://openalex.org/W6761203320","https://openalex.org/W6764037567","https://openalex.org/W6765543928","https://openalex.org/W6766609504","https://openalex.org/W6776488958","https://openalex.org/W6781361267"],"related_works":["https://openalex.org/W3062287","https://openalex.org/W2380390332","https://openalex.org/W2742145873","https://openalex.org/W4245975140","https://openalex.org/W1977763331","https://openalex.org/W2062253548","https://openalex.org/W4225414539","https://openalex.org/W4289522463","https://openalex.org/W4318483369","https://openalex.org/W2002560966"],"abstract_inverted_index":{"Graph":[0],"Convolutional":[1],"Network":[2],"(GCN)":[3],"has":[4],"drawn":[5],"considerable":[6],"research":[7],"attention":[8],"in":[9,26,60],"recent":[10],"times.":[11],"Many":[12],"different":[13,135],"problems":[14],"from":[15,138],"diverse":[16,139],"domains":[17],"can":[18],"be":[19],"solved":[20],"efficiently":[21],"using":[22,50,63,109,160],"GCN.":[23],"Community":[24],"detection":[25],"graphs":[27,62],"is":[28,68],"a":[29,39,51,85],"computationally":[30],"challenging":[31],"graph":[32,168,179],"analytic":[33],"problem.":[34],"The":[35],"presence":[36],"of":[37,42,177,191],"only":[38],"limited":[40],"amount":[41],"labelled":[43],"data":[44],"(known":[45],"communities)":[46],"motivates":[47],"us":[48],"for":[49,88,102,106,121],"learning":[52,65],"approach":[53],"to":[54,74,124,145],"community":[55],"discovery.":[56],"However,":[57],"detecting":[58,89,107],"communities":[59,90,108],"large":[61],"semi-supervised":[64,95,104],"with":[66,111,172,188],"GCN":[67,93],"still":[69],"an":[70,131],"open":[71],"problem":[72],"due":[73],"the":[75,100,126,167,183,189],"scalability":[76],"and":[77,148],"accuracy":[78,147,174],"issues.":[79],"In":[80],"this":[81],"paper,":[82],"we":[83],"present":[84],"scalable":[86],"method":[87],"based":[91,165],"on":[92,113,134,152,166],"via":[94],"node":[96],"classification.":[97],"We":[98,116,129,156,181],"optimize":[99],"hyper-parameters":[101],"our":[103],"model":[105,142,184],"PyTorch":[110],"CUDA":[112],"GPU":[114],"environment.":[115],"apply":[117],"Mini-batch":[118],"Gradient":[119],"Descent":[120],"larger":[122],"datasets":[123],"resolve":[125],"memory":[127],"issue.":[128],"demonstrate":[130],"experimental":[132],"evaluation":[133],"real-world":[136],"networks":[137],"domains.":[140],"Our":[141],"achieves":[143],"up":[144],"86.9%":[146],"0.85":[149],"F1":[150],"Score":[151],"these":[153],"practical":[154],"datasets.":[155],"also":[157],"show":[158],"that":[159,176],"identity":[161],"matrix":[162],"as":[163],"features,":[164],"connectivity,":[169],"performs":[170],"better":[171],"higher":[173],"than":[175],"vertex-based":[178],"features.":[180],"accelerate":[182],"performance":[185],"4":[186],"times":[187],"use":[190],"GPUs":[192],"over":[193],"CPUs.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
