{"id":"https://openalex.org/W2902163992","doi":"https://doi.org/10.1109/hpec.2018.8547735","title":"Discovering &lt;tex&gt;$k$&lt;/tex&gt;-Trusses in Large-Scale Networks","display_name":"Discovering &lt;tex&gt;$k$&lt;/tex&gt;-Trusses in Large-Scale Networks","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2902163992","doi":"https://doi.org/10.1109/hpec.2018.8547735","mag":"2902163992"},"language":"en","primary_location":{"id":"doi:10.1109/hpec.2018.8547735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2018.8547735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE High Performance extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5086573637","display_name":"Alessio Conte","orcid":"https://orcid.org/0000-0003-0770-2235"},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Alessio Conte","raw_affiliation_strings":["National Institute of Informatics, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Informatics, Tokyo, Japan","institution_ids":["https://openalex.org/I184597095"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056277459","display_name":"Daniele De Sensi","orcid":"https://orcid.org/0000-0002-7244-639X"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Daniele De Sensi","raw_affiliation_strings":["Universit\u00e0 di Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e0 di Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006984021","display_name":"Roberto Grossi","orcid":"https://orcid.org/0000-0002-7985-4222"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Roberto Grossi","raw_affiliation_strings":["Universit\u00e0 di Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e0 di Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035852380","display_name":"Andrea Marino","orcid":"https://orcid.org/0000-0002-9854-7885"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Andrea Marino","raw_affiliation_strings":["Universit\u00e0 di Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e0 di Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090111536","display_name":"Luca Versari","orcid":"https://orcid.org/0000-0003-3495-1325"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Versari","raw_affiliation_strings":["Universit\u00e0 di Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e0 di Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6979,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.86142118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"16","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9966999888420105,"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":0.9966999888420105,"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/T10374","display_name":"Advanced Graph Theory Research","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9832000136375427,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/truss","display_name":"Truss","score":0.6963546872138977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6466371417045593},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5199469327926636},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.480800062417984},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.47189411520957947},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.43259212374687195},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.38855481147766113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17677050828933716}],"concepts":[{"id":"https://openalex.org/C173534245","wikidata":"https://www.wikidata.org/wiki/Q1328068","display_name":"Truss","level":2,"score":0.6963546872138977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6466371417045593},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5199469327926636},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.480800062417984},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.47189411520957947},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.43259212374687195},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38855481147766113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17677050828933716},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/hpec.2018.8547735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2018.8547735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE High Performance extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/959914","is_oa":false,"landing_page_url":"http://hdl.handle.net/11568/959914","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:flore.unifi.it:2158/1149218","is_oa":false,"landing_page_url":"http://hdl.handle.net/2158/1149218","pdf_url":null,"source":{"id":"https://openalex.org/S4306402033","display_name":"Florence Research (University of Florence)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45084792","host_organization_name":"University of Florence","host_organization_lineage":["https://openalex.org/I45084792"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:iris.uniroma1.it:11573/1656233","is_oa":false,"landing_page_url":"https://hdl.handle.net/11573/1656233","pdf_url":null,"source":{"id":"https://openalex.org/S4377196107","display_name":"IRIS Research product catalog (Sapienza University of Rome)","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":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1489509891","https://openalex.org/W2249320699","https://openalex.org/W2258998514","https://openalex.org/W2275335637","https://openalex.org/W2738340603","https://openalex.org/W2762615846","https://openalex.org/W2765884153","https://openalex.org/W2766251751","https://openalex.org/W2766308819","https://openalex.org/W2804905035"],"related_works":["https://openalex.org/W2955404263","https://openalex.org/W2055188469","https://openalex.org/W2365888646","https://openalex.org/W4238655142","https://openalex.org/W2914493398","https://openalex.org/W2741416590","https://openalex.org/W2729632630","https://openalex.org/W2741963359","https://openalex.org/W2002630260","https://openalex.org/W933396280"],"abstract_inverted_index":{"A":[0],"k-truss":[1],"is":[2,40,199],"a":[3,32,38,76,85,114,128,137,175,187,209],"subgraph":[4],"where":[5],"every":[6],"edge":[7,22,29],"belongs":[8,30],"to":[9,31,88,204],"at":[10],"least":[11],"k-2":[12],"triangles":[13,90],"in":[14],"the":[15,23,28,35,41,100,134,149,172,194,200,206],"subgraph.":[16],"The":[17],"truss":[18,51,119],"decomposition":[19,52,120],"assigns":[20],"each":[21],"maximum":[24,42],"k":[25],"for":[26,48,56,118,163,193],"which":[27,121,132,190],"k-truss,":[33],"and":[34,50,142,160,179,196],"trussness":[36,135,207],"of":[37,80,103,136,151,177,208],"graph":[39,57,138,210],"among":[43],"its":[44],"edges.":[45,215],"Discovery":[46],"algorithms":[47,82,147,170],"k-trusses":[49],"provide":[53],"useful":[54],"insight":[55],"analytics":[58],"(such":[59],"as":[60],"community":[61],"detection).":[62],"Even":[63],"though":[64],"they":[65,72],"take":[66,148],"polynomial":[67],"time,":[68],"on":[69,99,105,174],"massive":[70],"networks":[71],"suffer":[73],"from":[74],"handling":[75],"potentially":[77],"cubic":[78],"number":[79,102],"wedges:":[81],"either":[83],"need":[84,162],"long":[86],"time":[87,141],"recompute":[89],"several":[91],"times,":[92],"have":[93],"high":[94],"memory":[95,158],"usage,":[96,159],"or":[97],"rely":[98],"large":[101],"cores":[104],"graphic":[106],"units.":[107],"In":[108],"this":[109],"paper":[110],"we":[111],"describe":[112],"EXTRUSS,":[113],"highly":[115],"optimized":[116],"algorithm":[117,202],"outperforms":[122],"existing":[123,152],"algorithms.":[124],"We":[125,167],"then":[126],"introduce":[127],"faster":[129],"algorithm,":[130],"HYBTRUSS,":[131],"finds":[133],"using":[139],"less":[140],"space":[143],"than":[144],"EXTRUSS.":[145],"Our":[146],"best":[150],"approaches":[153],"having":[154],"good":[155],"performance,":[156],"low":[157],"no":[161],"sophisticated":[164],"hardware":[165],"systems.":[166],"compare":[168],"our":[169,197],"with":[171,185,211],"state-of-the-art":[173],"set":[176],"real-world":[178],"synthetic":[180],"networks.":[181],"EXTRUSS":[182],"processes":[183],"graphs":[184],"over":[186,212],"billion":[188,214],"edges,":[189],"seems":[191],"difficult":[192],"competitors,":[195],"HYBTRUSS":[198],"first":[201],"able":[203],"find":[205],"25":[213]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2018-12-11T00:00:00"}
