{"id":"https://openalex.org/W4228999789","doi":"https://doi.org/10.1145/3477314.3507018","title":"Combining clutter reduction methods for temporal network visualization","display_name":"Combining clutter reduction methods for temporal network visualization","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4228999789","doi":"https://doi.org/10.1145/3477314.3507018"},"language":"en","primary_location":{"id":"doi:10.1145/3477314.3507018","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477314.3507018","pdf_url":null,"source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","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/A5008364265","display_name":"Jean R. Ponciano","orcid":"https://orcid.org/0000-0003-4629-3542"},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jean R. Ponciano","raw_affiliation_strings":["University of Uberl\u00e2ndia, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Uberl\u00e2ndia, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061817648","display_name":"Cl\u00e1udio D. G. Linhares","orcid":"https://orcid.org/0000-0001-7012-4461"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Claudio D. G. Linhares","raw_affiliation_strings":["University of S\u00e3o, Paulo, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of S\u00e3o, Paulo, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032045555","display_name":"Luis E. C. Rocha","orcid":"https://orcid.org/0000-0001-9046-8739"},"institutions":[{"id":"https://openalex.org/I2801227569","display_name":"Ghent University Hospital","ror":"https://ror.org/00xmkp704","country_code":"BE","type":"healthcare","lineage":["https://openalex.org/I2801227569"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Luis E. C. Rocha","raw_affiliation_strings":["Ghent University, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ghent University, Belgium","institution_ids":["https://openalex.org/I2801227569"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042178956","display_name":"Elaine R. Faria","orcid":"https://orcid.org/0000-0001-5242-9026"},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Elaine R. Faria","raw_affiliation_strings":["University of Uberl\u00e2ndia, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Uberl\u00e2ndia, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044390168","display_name":"Bruno Augusto Nassif Traven\u00e7olo","orcid":"https://orcid.org/0000-0001-7690-301X"},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bruno A. N. Traven\u00e7olo","raw_affiliation_strings":["University of Uberl\u00e2ndia, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Uberl\u00e2ndia, Brazil","institution_ids":["https://openalex.org/I80850581"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2359,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.5604458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1748","last_page":"1755"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9958000183105469,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9958000183105469,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9955999851226807,"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/T10487","display_name":"Plant and animal studies","score":0.9663000106811523,"subfield":{"id":"https://openalex.org/subfields/1105","display_name":"Ecology, Evolution, Behavior and Systematics"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8104867935180664},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.8066521883010864},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.7472425699234009},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.6360200047492981},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6219484210014343},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5376153588294983},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5293980836868286},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.515972912311554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45868492126464844},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4354686737060547},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4238017201423645},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.20140230655670166},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.16451841592788696},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09089168906211853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8104867935180664},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.8066521883010864},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.7472425699234009},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.6360200047492981},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6219484210014343},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5376153588294983},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5293980836868286},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.515972912311554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45868492126464844},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4354686737060547},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4238017201423645},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.20140230655670166},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.16451841592788696},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09089168906211853},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3477314.3507018","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477314.3507018","pdf_url":null,"source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:01GQPYSHSXQ60S2EY6YX3F225G","is_oa":false,"landing_page_url":"http://hdl.handle.net/1854/LU-01GQPYSHSXQ60S2EY6YX3F225G","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9781450387132","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1544632947","https://openalex.org/W1553409264","https://openalex.org/W1975443659","https://openalex.org/W2043690777","https://openalex.org/W2073256825","https://openalex.org/W2104592282","https://openalex.org/W2114082014","https://openalex.org/W2123098252","https://openalex.org/W2127048411","https://openalex.org/W2131681506","https://openalex.org/W2145428101","https://openalex.org/W2154656661","https://openalex.org/W2157316473","https://openalex.org/W2336754337","https://openalex.org/W2487521875","https://openalex.org/W2620734284","https://openalex.org/W2626475055","https://openalex.org/W2725191915","https://openalex.org/W2793117549","https://openalex.org/W2890433923","https://openalex.org/W2970728072","https://openalex.org/W2982528360","https://openalex.org/W2995758823","https://openalex.org/W3037819556","https://openalex.org/W3087363341","https://openalex.org/W3088290848","https://openalex.org/W3102641634","https://openalex.org/W3146512515","https://openalex.org/W3157495313","https://openalex.org/W3172094211","https://openalex.org/W4200225383","https://openalex.org/W4297439921"],"related_works":["https://openalex.org/W2060561905","https://openalex.org/W1417711376","https://openalex.org/W1986883493","https://openalex.org/W2130674020","https://openalex.org/W2093748878","https://openalex.org/W2333771223","https://openalex.org/W2469862403","https://openalex.org/W2120056845","https://openalex.org/W2166378262","https://openalex.org/W2035891203"],"abstract_inverted_index":{"Temporal":[0],"network":[1,12],"visualization":[2,152],"is":[3,26],"a":[4,32,178,191,197],"powerful":[5],"tool":[6],"that":[7,35,92,184],"assists":[8],"users":[9],"in":[10,23,82,100,137,150,172],"understanding":[11],"structure":[13],"and":[14,42,117,128],"dynamics.":[15],"One":[16],"of":[17,39,66,135,140,147,165,177],"the":[18,27,37,63,73,83,138,145,151,163,175],"most":[19],"popular":[20],"visual":[21,54,102],"representations":[22],"this":[24,109],"context":[25,139],"Massive":[28],"Sequence":[29],"View":[30],"(MSV),":[31],"timeline-based":[33],"layout":[34,126],"allows":[36],"identification":[38],"patterns,":[40],"anomalies,":[41],"other":[43],"structures":[44],"from":[45,53],"global":[46],"to":[47,58,62,97,120,143,174],"local":[48],"scales.":[49],"MSV":[50,136],"may":[51],"suffer":[52],"clutter":[55,76,103],"when":[56],"applied":[57],"real-world":[59,159],"networks":[60],"due":[61],"large":[64],"number":[65],"nodes,":[67],"edges,":[68],"and/or":[69],"timestamps.":[70],"To":[71],"enhance":[72],"analysis,":[74],"several":[75],"reduction":[77],"methods":[78,94,119,171],"have":[79],"been":[80],"proposed":[81],"literature.":[84],"No":[85],"study,":[86],"however,":[87],"has":[88],"evaluated":[89],"different":[90,148],"strategies":[91],"combine":[93,112],"with":[95,158,196],"respect":[96],"their":[98],"effectiveness":[99],"reducing":[101],"while":[104],"highlighting":[105],"meaningful":[106],"patterns.":[107],"In":[108],"paper,":[110],"we":[111,161],"node":[113,199],"ordering,":[114],"edge":[115,185],"sampling,":[116],"timeslicing":[118],"analyze":[121],"how":[122],"these":[123],"combinations":[124,149],"impact":[125],"readability":[127],"pattern":[129],"identification.":[130],"We":[131,181],"consider":[132],"recent":[133],"applications":[134],"infection":[141],"dynamics":[142],"study":[144],"effect":[146],"layout.":[153],"Through":[154],"two":[155,169],"case":[156],"studies":[157],"networks,":[160],"demonstrate":[162],"superiority":[164],"combining":[166],"at":[167],"least":[168],"high-quality":[170],"relation":[173],"use":[176],"single":[179],"method.":[180],"also":[182],"show":[183],"sampling":[186],"should":[187],"be":[188],"used":[189],"as":[190],"complementary":[192],"strategy,":[193],"always":[194],"associated":[195],"high-performance":[198],"ordering.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}