{"id":"https://openalex.org/W4402156519","doi":"https://doi.org/10.1109/icc51166.2024.10622572","title":"Multi-Dimensional Threshold Model With Correlation: Emergence of Global Cascades","display_name":"Multi-Dimensional Threshold Model With Correlation: Emergence of Global Cascades","publication_year":2024,"publication_date":"2024-06-09","ids":{"openalex":"https://openalex.org/W4402156519","doi":"https://doi.org/10.1109/icc51166.2024.10622572"},"language":"en","primary_location":{"id":"doi:10.1109/icc51166.2024.10622572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc51166.2024.10622572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","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/A5073401793","display_name":"Yurun Tian","orcid":"https://orcid.org/0000-0002-9518-3430"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yurun Tian","raw_affiliation_strings":["Carnegie Mellon University,Electrical and Computer Engineering,Pittsburgh,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University,Electrical and Computer Engineering,Pittsburgh,USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064066193","display_name":"Osman Ya\u011fan","orcid":"https://orcid.org/0000-0002-7057-2966"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Osman Ya\u011fan","raw_affiliation_strings":["Carnegie Mellon University,Electrical and Computer Engineering,Pittsburgh,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University,Electrical and Computer Engineering,Pittsburgh,USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2503,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5288131,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"533","last_page":"538"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9934999942779541,"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/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9934999942779541,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.96670001745224,"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/correlation","display_name":"Correlation","score":0.6065853834152222},{"id":"https://openalex.org/keywords/threshold-model","display_name":"Threshold model","score":0.5133739709854126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4767882823944092},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.4227057099342346},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2056211233139038},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16524869203567505},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09757456183433533}],"concepts":[{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6065853834152222},{"id":"https://openalex.org/C202632270","wikidata":"https://www.wikidata.org/wiki/Q7798106","display_name":"Threshold model","level":2,"score":0.5133739709854126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4767882823944092},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.4227057099342346},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2056211233139038},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16524869203567505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09757456183433533},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc51166.2024.10622572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc51166.2024.10622572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6299999952316284,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G5176628845","display_name":null,"funder_award_id":"CIF-#2225513","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6937675914","display_name":null,"funder_award_id":"#FA9550-22-1-0233","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G7465051342","display_name":null,"funder_award_id":"#W911NF-22-1-0181","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1502487903","https://openalex.org/W1963983289","https://openalex.org/W1977878271","https://openalex.org/W1989640353","https://openalex.org/W1989960380","https://openalex.org/W1993285489","https://openalex.org/W2010478496","https://openalex.org/W2027367074","https://openalex.org/W2044881936","https://openalex.org/W2060637692","https://openalex.org/W2081989682","https://openalex.org/W2114696370","https://openalex.org/W2122803078","https://openalex.org/W2130801612","https://openalex.org/W2130863673","https://openalex.org/W2147453867","https://openalex.org/W2154169270","https://openalex.org/W2169015768","https://openalex.org/W2513032237","https://openalex.org/W2535260614","https://openalex.org/W2762079755","https://openalex.org/W2783906594","https://openalex.org/W2807779271","https://openalex.org/W2959322849","https://openalex.org/W3104100506","https://openalex.org/W4206758461","https://openalex.org/W4385197983"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"The":[0],"study":[1],"of":[2,34,90,114,116],"complex":[3,62,149],"contagions":[4,63,150],"over":[5,59,103,151],"networks":[6],"has":[7],"been":[8],"receiving":[9],"increasing":[10],"attention":[11],"across":[12],"many":[13,75],"scientific":[14],"domains.":[15],"Especially,":[16],"linear":[17,93],"threshold":[18,85,94],"models":[19],"are":[20,36],"widely":[21],"studied":[22],"due":[23],"to":[24,27,43,96],"the":[25,29,41,91,125,128,136],"ability":[26],"capture":[28],"mechanism":[30],"that":[31,110],"multiple":[32,66,98],"sources":[33],"exposure":[35],"required":[37],"for":[38,74,119,148],"nodes":[39],"in":[40],"network":[42,130],"take":[44],"action.":[45],"Most":[46],"related":[47],"works":[48],"on":[49,54,135],"influence":[50],"propagation":[51],"only":[52],"concentrate":[53],"a":[55,83],"single":[56],"content":[57],"spreading":[58,69,101,137],"networks.":[60,104],"However,":[61],"usually":[64],"involve":[65],"correlated":[67,99,120],"contents":[68,100],"simultaneously,":[70],"demonstrating":[71],"significant":[72],"implications":[73],"real-life":[76],"systems.":[77],"In":[78],"this":[79],"work,":[80],"we":[81],"propose":[82],"multi-dimensional":[84],"model":[86,95],"as":[87],"an":[88],"extension":[89],"classical":[92],"incorporate":[97],"simultaneously":[102],"We":[105],"also":[106],"provide":[107],"analytical":[108],"results":[109,123],"accurately":[111],"predict":[112],"probability":[113],"emergence":[115],"global":[117],"cascades":[118],"contents.":[121],"These":[122],"reveal":[124],"interplay":[126],"between":[127],"underlying":[129],"structure":[131],"and":[132,145],"contents'":[133],"correlation":[134],"processes.":[138],"Thus,":[139],"our":[140],"work":[141],"advances":[142],"analysis,":[143],"prediction":[144],"control":[146],"strategies":[147],"networks,":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}