{"id":"https://openalex.org/W2886935179","doi":"https://doi.org/10.1145/3230743","title":"I/O-Efficient Generation of Massive Graphs Following the <i>LFR</i> Benchmark","display_name":"I/O-Efficient Generation of Massive Graphs Following the <i>LFR</i> Benchmark","publication_year":2018,"publication_date":"2018-08-09","ids":{"openalex":"https://openalex.org/W2886935179","doi":"https://doi.org/10.1145/3230743","mag":"2886935179"},"language":"en","primary_location":{"id":"doi:10.1145/3230743","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3230743","pdf_url":null,"source":{"id":"https://openalex.org/S201104086","display_name":"ACM Journal of Experimental Algorithmics","issn_l":"1084-6654","issn":["1084-6654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Journal of Experimental Algorithmics","raw_type":"journal-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/A5084195614","display_name":"Michael Hamann","orcid":"https://orcid.org/0000-0002-6958-4927"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Hamann","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103183708","display_name":"Ulrich Meyer","orcid":"https://orcid.org/0000-0002-1197-3153"},"institutions":[{"id":"https://openalex.org/I114090438","display_name":"Goethe University Frankfurt","ror":"https://ror.org/04cvxnb49","country_code":"DE","type":"education","lineage":["https://openalex.org/I114090438"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ulrich Meyer","raw_affiliation_strings":["Goethe University Frankfurt, Frankfurt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goethe University Frankfurt, Frankfurt, Germany","institution_ids":["https://openalex.org/I114090438"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052142376","display_name":"Manuel Penschuck","orcid":"https://orcid.org/0000-0003-2630-7548"},"institutions":[{"id":"https://openalex.org/I114090438","display_name":"Goethe University Frankfurt","ror":"https://ror.org/04cvxnb49","country_code":"DE","type":"education","lineage":["https://openalex.org/I114090438"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Manuel Penschuck","raw_affiliation_strings":["Goethe University Frankfurt, Frankfurt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goethe University Frankfurt, Frankfurt, Germany","institution_ids":["https://openalex.org/I114090438"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000189848","display_name":"Hung Tran","orcid":"https://orcid.org/0000-0001-6836-5151"},"institutions":[{"id":"https://openalex.org/I114090438","display_name":"Goethe University Frankfurt","ror":"https://ror.org/04cvxnb49","country_code":"DE","type":"education","lineage":["https://openalex.org/I114090438"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hung Tran","raw_affiliation_strings":["Goethe University Frankfurt, Frankfurt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goethe University Frankfurt, Frankfurt, Germany","institution_ids":["https://openalex.org/I114090438"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045460793","display_name":"Dorothea Wagner","orcid":"https://orcid.org/0000-0002-9141-7076"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dorothea Wagner","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9228,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.74696725,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"23","issue":null,"first_page":"1","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9962999820709229,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.6662837266921997},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6216949820518494},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4529229700565338},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.4230669140815735},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4050058126449585},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3998256325721741},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.38756605982780457}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6662837266921997},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6216949820518494},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4529229700565338},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.4230669140815735},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4050058126449585},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3998256325721741},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.38756605982780457},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3230743","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3230743","pdf_url":null,"source":{"id":"https://openalex.org/S201104086","display_name":"ACM Journal of Experimental Algorithmics","issn_l":"1084-6654","issn":["1084-6654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Journal of Experimental Algorithmics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W88065483","https://openalex.org/W154688724","https://openalex.org/W324754135","https://openalex.org/W834693420","https://openalex.org/W1189676379","https://openalex.org/W1487258612","https://openalex.org/W1565608089","https://openalex.org/W1591568670","https://openalex.org/W1623104014","https://openalex.org/W1888264395","https://openalex.org/W1893161742","https://openalex.org/W1969575089","https://openalex.org/W1970301364","https://openalex.org/W1984431017","https://openalex.org/W1991408655","https://openalex.org/W1995996823","https://openalex.org/W1997914438","https://openalex.org/W2001236100","https://openalex.org/W2018480107","https://openalex.org/W2023655578","https://openalex.org/W2038142281","https://openalex.org/W2040793975","https://openalex.org/W2049446489","https://openalex.org/W2049742196","https://openalex.org/W2058357179","https://openalex.org/W2082459856","https://openalex.org/W2095293504","https://openalex.org/W2111708605","https://openalex.org/W2117863112","https://openalex.org/W2124209874","https://openalex.org/W2127048411","https://openalex.org/W2127620007","https://openalex.org/W2128366083","https://openalex.org/W2131681506","https://openalex.org/W2144022036","https://openalex.org/W2293947850","https://openalex.org/W2401005551","https://openalex.org/W2404379443","https://openalex.org/W2732288815","https://openalex.org/W2765833713","https://openalex.org/W2951271819","https://openalex.org/W2963794747","https://openalex.org/W3041685368","https://openalex.org/W3099768174","https://openalex.org/W3102641634","https://openalex.org/W3105561612","https://openalex.org/W3126033509","https://openalex.org/W3148153833","https://openalex.org/W4205469090","https://openalex.org/W4235169531","https://openalex.org/W4238452917","https://openalex.org/W4291439952"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W2086519370","https://openalex.org/W2028665553","https://openalex.org/W2087343574","https://openalex.org/W2535915176","https://openalex.org/W2105860728","https://openalex.org/W4287657826"],"abstract_inverted_index":{"LFR":[0,30],"is":[1,36,115,130,139,157,200],"a":[2,101,127,133,142],"popular":[3],"benchmark":[4],"graph":[5,162,196],"generator":[6],"used":[7],"to":[8,23,99,117,187],"evaluate":[9],"community":[10],"detection":[11],"algorithms.":[12],"We":[13,83,172],"present":[14],"EM-LFR":[15,154],",":[16,73,87],"the":[17,29,37,53,60,93,169,193,210,215],"first":[18,56],"external":[19,76],"memory":[20,77,145],"algorithm":[21],"able":[22,116],"generate":[24],"massive":[25],"complex":[26],"networks":[27,203],"following":[28],"benchmark.":[31],"Its":[32],"most":[33],"expensive":[34],"component":[35],"generation":[38],"of":[39,159,165,195],"random":[40,102],"graphs":[41,54,119,179],"with":[42,120,132,180,205],"prescribed":[43],"degree":[44],"sequences":[45],"which":[46],"can":[47,213],"be":[48],"divided":[49],"into":[50],"two":[51,74,81],"steps:":[52],"are":[55,69],"materialized":[57],"deterministically":[58],"using":[59,92],"Havel-Hakimi":[61],"algorithm,":[62,137],"and":[63,71,96,138,207],"then":[64],"randomized.":[65],"Our":[66],"main":[67,152],"contributions":[68],"EM-HH":[70],"EM-ES":[72,199],"I/O-efficient":[75],"algorithms":[78,186],"for":[79],"these":[80],"steps.":[82],"also":[84],"propose":[85],"EM-CM/ES":[86,206],"an":[88,106],"alternative":[89,211],"sampling":[90,216],"scheme":[91],"Configuration":[94],"Model":[95],"rewiring":[97],"steps":[98],"obtain":[100],"simple":[103],"graph.":[104],"In":[105],"experimental":[107],"evaluation,":[108],"we":[109,191],"demonstrate":[110],"their":[111],"performance;":[112],"our":[113],"implementation":[114,146,156],"handle":[118],"more":[121],"than":[122,141,168],"37":[123],"billion":[124],"edges":[125],"on":[126,148,202],"single":[128],"machine,":[129],"competitive":[131],"massively":[134],"parallel":[135],"distributed":[136],"faster":[140,167],"state-of-the-art":[143],"internal":[144],"even":[147],"instances":[149,163],"fitting":[150],"in":[151],"memory.":[153],"\u2019s":[155],"capable":[158],"generating":[160],"large":[161],"orders":[164],"magnitude":[166],"original":[170],"implementation.":[171],"give":[173],"evidence":[174],"that":[175,209],"both":[176],"implementations":[177],"yield":[178],"matching":[181],"properties":[182,197],"by":[183],"applying":[184],"clustering":[185],"generated":[188],"instances.":[189],"Similarly,":[190],"analyze":[192],"evolution":[194],"as":[198],"executed":[201],"obtained":[204],"find":[208],"approach":[212],"accelerate":[214],"process.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
