{"id":"https://openalex.org/W4205912034","doi":"https://doi.org/10.1109/bigdata52589.2021.9671512","title":"Correlation and pattern detection in event networks","display_name":"Correlation and pattern detection in event networks","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205912034","doi":"https://doi.org/10.1109/bigdata52589.2021.9671512"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671512","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","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/A5052021169","display_name":"Valerio Bellandi","orcid":"https://orcid.org/0000-0003-4473-6258"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Valerio Bellandi","raw_affiliation_strings":["Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030929146","display_name":"Paolo Ceravolo","orcid":"https://orcid.org/0000-0002-4519-0173"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Ceravolo","raw_affiliation_strings":["Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002165190","display_name":"Samira Maghool","orcid":"https://orcid.org/0000-0001-8310-2050"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Samira Maghool","raw_affiliation_strings":["Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045503590","display_name":"Margherita Pindaro","orcid":null},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Margherita Pindaro","raw_affiliation_strings":["Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040709924","display_name":"Stefano Siccardi","orcid":"https://orcid.org/0000-0002-6477-3876"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Stefano Siccardi","raw_affiliation_strings":["Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universit\u00e0 degli Studi di Milano, Milan, Italy","institution_ids":["https://openalex.org/I189158943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052021169"],"corresponding_institution_ids":["https://openalex.org/I189158943"],"apc_list":null,"apc_paid":null,"fwci":1.7094,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.85509839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4103","last_page":"4112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9961000084877014,"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":0.9961000084877014,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7282780408859253},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.659007728099823},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6196213960647583},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5518379211425781},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.49232590198516846},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49151286482810974},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4609646499156952},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4532630443572998},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4009477198123932},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3711152672767639},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1607750654220581}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7282780408859253},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.659007728099823},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6196213960647583},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5518379211425781},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.49232590198516846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49151286482810974},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4609646499156952},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4532630443572998},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4009477198123932},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3711152672767639},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1607750654220581},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671512","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W194141526","https://openalex.org/W2093168265","https://openalex.org/W2612872092","https://openalex.org/W2800593390","https://openalex.org/W2922924332","https://openalex.org/W2955202322","https://openalex.org/W2963405399","https://openalex.org/W2966573219","https://openalex.org/W2969696968","https://openalex.org/W2973088434","https://openalex.org/W3018483767","https://openalex.org/W3022959799","https://openalex.org/W3101358844","https://openalex.org/W3108820632","https://openalex.org/W3123217666","https://openalex.org/W3140934596","https://openalex.org/W4237820780","https://openalex.org/W6607860558","https://openalex.org/W6751278833","https://openalex.org/W6767840206","https://openalex.org/W6785794062"],"related_works":["https://openalex.org/W2798715693","https://openalex.org/W3103753037","https://openalex.org/W4317655900","https://openalex.org/W4287763734","https://openalex.org/W3035116611","https://openalex.org/W3114961909","https://openalex.org/W4226361842","https://openalex.org/W2923818335","https://openalex.org/W4214591239","https://openalex.org/W4312815851"],"abstract_inverted_index":{"Events":[0],"happening":[1],"at":[2],"defined":[3],"moments":[4],"in":[5,20,55,80,89],"time":[6],"and":[7,68,75,95,98,118],"involving":[8],"specific":[9],"entities":[10],"from":[11,114],"a":[12,115],"social":[13],"or":[14,22,37,51],"physical":[15],"system":[16],"can":[17,53],"be":[18],"organized":[19],"networks":[21],"graphs.":[23],"The":[24],"study":[25],"of":[26,49,57],"such":[27],"event":[28,66],"graphs":[29],"may":[30],"reveal":[31],"causal":[32],"relations":[33],"between":[34],"subsequent":[35],"events":[36,39,50,74,88],"compound":[38],"that":[40],"we":[41],"define":[42],"as":[43],"\u201ctyped":[44],"events\u201d.":[45],"Moreover,":[46],"characteristic":[47],"sequences":[48],"patterns":[52],"arise":[54],"consequence":[56],"phenomena":[58],"affecting":[59],"the":[60,65,72],"system.":[61],"Methods":[62],"to":[63,69,85,103],"build":[64],"graph":[67],"search":[70,104],"for":[71,105],"typed":[73,87],"their":[76],"significance":[77],"are":[78],"described":[79],"detail.":[81],"An":[82],"embedding":[83],"strategy":[84],"encode":[86],"low":[90],"dimensional":[91],"vectors":[92],"is":[93,101],"defined,":[94],"both":[96],"supervised":[97],"unsupervised":[99],"learning":[100],"applied":[102],"meaningful":[106],"patterns.":[107],"Experiments":[108],"have":[109],"been":[110],"conducted":[111],"using":[112],"data":[113],"real":[116],"investigation":[117],"some":[119],"synthetic":[120],"data.":[121]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":5}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}
