{"id":"https://openalex.org/W4406458070","doi":"https://doi.org/10.1109/bigdata62323.2024.10825757","title":"PCCMerge: a parallel method based on merging partial connected components in large graphs","display_name":"PCCMerge: a parallel method based on merging partial connected components in large graphs","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458070","doi":"https://doi.org/10.1109/bigdata62323.2024.10825757"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5109058831","display_name":"Hubert Naacke","orcid":null},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210159731","display_name":"LIP6","ror":"https://ror.org/05krcen59","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I39804081","https://openalex.org/I4210159245","https://openalex.org/I4210159731"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Hubert Naacke","raw_affiliation_strings":["Sorbonne Université - LIP6,Paris,France"],"affiliations":[{"raw_affiliation_string":"Sorbonne Université - LIP6,Paris,France","institution_ids":["https://openalex.org/I4210159731","https://openalex.org/I39804081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115904472","display_name":"No\u00e9 Cur\u00e9","orcid":null},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210159731","display_name":"LIP6","ror":"https://ror.org/05krcen59","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I39804081","https://openalex.org/I4210159245","https://openalex.org/I4210159731"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"No\u00e9 Cur\u00e9","raw_affiliation_strings":["Sorbonne Université - LIP6,Paris,France"],"affiliations":[{"raw_affiliation_string":"Sorbonne Université - LIP6,Paris,France","institution_ids":["https://openalex.org/I4210159731","https://openalex.org/I39804081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112101676","display_name":"Olivier Cur\u00e9","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210152518","display_name":"Laboratoire d'Informatique Gaspard-Monge","ror":"https://ror.org/04t50yk91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I142631665","https://openalex.org/I4210145102","https://openalex.org/I4210152518","https://openalex.org/I4210154111","https://openalex.org/I4210159245"]},{"id":"https://openalex.org/I4210154111","display_name":"Universit\u00e9 Gustave Eiffel","ror":"https://ror.org/03x42jk29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210154111"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Olivier Cur\u00e9","raw_affiliation_strings":["LIGM Univ Gustave Eiffel,CNRS,Marne la Vallée,France"],"affiliations":[{"raw_affiliation_string":"LIGM Univ Gustave Eiffel,CNRS,Marne la Vallée,France","institution_ids":["https://openalex.org/I4210152518","https://openalex.org/I1294671590","https://openalex.org/I4210154111"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5109058831"],"corresponding_institution_ids":["https://openalex.org/I39804081","https://openalex.org/I4210159731"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26251248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4068","last_page":"4077"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9998999834060669,"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/T10829","display_name":"Interconnection Networks and Systems","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9796000123023987,"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":0.7453150749206543},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.44355663657188416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7453150749206543},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.44355663657188416}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04946604v1","is_oa":false,"landing_page_url":"https://hal.sorbonne-universite.fr/hal-04946604","pdf_url":null,"source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE BigData Next-Generation Big Graph Learning and Analytics (Next-Gen-BiGLA), Dec 2024, Washington D.C., United States. pp.4068-4077, ⟨10.1109/BigData62323.2024.10825757⟩","raw_type":"Conference papers"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W229097380","https://openalex.org/W1628302504","https://openalex.org/W1984775058","https://openalex.org/W2088011174","https://openalex.org/W2170616854","https://openalex.org/W2291321158","https://openalex.org/W2491983741","https://openalex.org/W2584312932","https://openalex.org/W2783930193","https://openalex.org/W3002924435","https://openalex.org/W3100078588","https://openalex.org/W4322772222","https://openalex.org/W4391054884","https://openalex.org/W6608852248","https://openalex.org/W6628546715","https://openalex.org/W6636680023","https://openalex.org/W6776488958"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Graphs":[0],"are":[1,78],"fundamental":[2],"data":[3,24,178],"structures":[4],"used":[5,80],"in":[6,44,60,138,174],"a":[7,32,74,87,92,104,109,139,160,172],"wide":[8,33],"range":[9,34],"of":[10,35,40,48,51,62,73,86,103,118,135,177],"fields,":[11],"from":[12],"social":[13],"networks":[14],"to":[15,21,81,96,127,150,171],"bioinformatics.":[16],"Their":[17],"flexibility":[18],"and":[19,55,66,108],"ability":[20],"model":[22],"complex":[23],"make":[25],"them":[26],"an":[27,124],"indispensable":[28],"tool":[29],"for":[30],"solving":[31],"problems.":[36],"However,":[37],"the":[38,46,57,71,83,119,133,154,175],"use":[39,142],"graphs":[41],"poses":[42],"challenges,":[43],"particular":[45],"management":[47],"large":[49],"amounts":[50],"data,":[52],"which":[53,77],"complicates":[54],"increases":[56],"analysis":[58],"load":[59],"terms":[61],"computational":[63],"power,":[64],"memory":[65],"storage.":[67],"This":[68,89,165],"paper":[69,90],"addresses":[70],"computation":[72],"connected":[75],"components,":[76],"often":[79],"understand":[82],"connectivity":[84],"structure":[85],"graph.":[88],"proposes":[91],"novel":[93],"parallel":[94],"approach":[95,126,137,146],"perform":[97],"this":[98],"computation.":[99],"It":[100],"takes":[101],"advantage":[102],"cluster":[105,162],"computing":[106,128,163],"engine":[107],"graph":[110],"processing":[111],"library":[112],"running":[113],"locally":[114],"on":[115,159],"each":[116],"node":[117],"cluster.":[120],"We":[121,131],"also":[122],"present":[123],"incremental":[125],"these":[129],"components.":[130],"demonstrate":[132],"efficiency":[134],"our":[136,145],"real":[140],"biological":[141],"case,":[143],"where":[144],"can":[147],"be":[148],"up":[149],"4x":[151],"faster":[152],"than":[153],"label":[155],"propagation":[156],"algorithm":[157],"implemented":[158],"state-of-the-art":[161],"engine.":[164],"performance":[166],"gain":[167],"is":[168],"mainly":[169],"due":[170],"reduction":[173],"amount":[176],"shuffling.":[179]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}