{"id":"https://openalex.org/W3035516244","doi":"https://doi.org/10.1140/epjds/s13688-020-00233-y","title":"Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection","display_name":"Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3035516244","doi":"https://doi.org/10.1140/epjds/s13688-020-00233-y","mag":"3035516244"},"language":"en","primary_location":{"id":"doi:10.1140/epjds/s13688-020-00233-y","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-020-00233-y","pdf_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-020-00233-y","source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPJ Data Science","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-020-00233-y","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035700260","display_name":"Mandana Saebi","orcid":"https://orcid.org/0000-0002-9839-9173"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mandana Saebi","raw_affiliation_strings":["University of Notre Dame, Notre Dame, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101613464","display_name":"Jian Xu","orcid":"https://orcid.org/0000-0001-6201-9215"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Xu","raw_affiliation_strings":["University of Notre Dame, Notre Dame, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022713560","display_name":"Lance Kaplan","orcid":"https://orcid.org/0000-0002-3627-4471"},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lance M. Kaplan","raw_affiliation_strings":["U.S. Army Research Lab, Adelphi, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"U.S. Army Research Lab, Adelphi, USA","institution_ids":["https://openalex.org/I166416128"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035917702","display_name":"Bruno Ribeiro","orcid":"https://orcid.org/0000-0002-3527-6192"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bruno Ribeiro","raw_affiliation_strings":["Purdue University, West Lafayette, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068157871","display_name":"Nitesh V. Chawla","orcid":"https://orcid.org/0000-0003-3932-5956"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nitesh V. Chawla","raw_affiliation_strings":["University of Notre Dame, Notre Dame, USA"],"raw_orcid":"https://orcid.org/0000-0003-3932-5956","affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068157871"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":{"value":1690,"currency":"USD","value_usd":1690},"apc_paid":{"value":1690,"currency":"USD","value_usd":1690},"fwci":3.0859,"has_fulltext":true,"cited_by_count":43,"citation_normalized_percentile":{"value":0.92968307,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"9","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9973999857902527,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7395267486572266},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5725017189979553},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5693513751029968},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5440977215766907},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.5073384642601013},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3469664454460144},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3226620852947235},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10777419805526733}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7395267486572266},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5725017189979553},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5693513751029968},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5440977215766907},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.5073384642601013},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3469664454460144},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3226620852947235},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10777419805526733},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1140/epjds/s13688-020-00233-y","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-020-00233-y","pdf_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-020-00233-y","source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPJ Data Science","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1712.09658","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1712.09658","pdf_url":"https://arxiv.org/pdf/1712.09658","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"pmh:oai:doaj.org/article:6c5749a5dc5945878d1b70efe5591f85","is_oa":true,"landing_page_url":"https://doaj.org/article/6c5749a5dc5945878d1b70efe5591f85","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EPJ Data Science, Vol 9, Iss 1, Pp 1-22 (2020)","raw_type":"article"},{"id":"pmh:oai:figshare.com:article/24740142","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Efficient_modeling_of_higher-order_dependencies_in_networks_from_algorithm_to_application_for_anomaly_detection/24740142","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1140/epjds/s13688-020-00233-y","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-020-00233-y","pdf_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-020-00233-y","source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPJ Data Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1162675597","display_name":null,"funder_award_id":"-1629914","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3142689400","display_name":null,"funder_award_id":"IIS-1447795","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4029481580","display_name":"BIGDATA: F: DKM: Addressing the two V's of Veracity and Variety in Big Data","funder_award_id":"1447795","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4360170584","display_name":"II-New: Infrastructure for Supporting Biomedical Application Algorithms, Runtime Development and Resource Management","funder_award_id":"1629914","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5259331294","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"},{"id":"https://openalex.org/G5737121807","display_name":null,"funder_award_id":"EF-1427157","funder_id":"https://openalex.org/F4320310260","funder_display_name":"University of Notre Dame"},{"id":"https://openalex.org/G6933719219","display_name":"Coastal SEES Collaborative Research: Changes in Ship-borne Introductions of Invasive Species in Coupled Natural-human Systems: Infrastructure, Global Trade, Climate and Policy","funder_award_id":"1427157","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7472522187","display_name":null,"funder_award_id":"EF-1427157","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7561134949","display_name":null,"funder_award_id":"W911NF-09-2-0053","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7968831398","display_name":null,"funder_award_id":"CRI-1629914","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G948678646","display_name":null,"funder_award_id":"W911NF-09-2-0053","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320310260","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3035516244.pdf","grobid_xml":"https://content.openalex.org/works/W3035516244.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1492581097","https://openalex.org/W1860746938","https://openalex.org/W1965555277","https://openalex.org/W2018213620","https://openalex.org/W2019658525","https://openalex.org/W2020711986","https://openalex.org/W2021046139","https://openalex.org/W2038819732","https://openalex.org/W2044555216","https://openalex.org/W2089554624","https://openalex.org/W2093168265","https://openalex.org/W2097363716","https://openalex.org/W2107952582","https://openalex.org/W2137556660","https://openalex.org/W2157547297","https://openalex.org/W2157762631","https://openalex.org/W2199717152","https://openalex.org/W2247894828","https://openalex.org/W2298103042","https://openalex.org/W2321942568","https://openalex.org/W2342114889","https://openalex.org/W2406996709","https://openalex.org/W2470861207","https://openalex.org/W2592860192","https://openalex.org/W2752404929","https://openalex.org/W2910193396","https://openalex.org/W2924173387","https://openalex.org/W2958292454","https://openalex.org/W2963785568","https://openalex.org/W3037826211","https://openalex.org/W3080627911","https://openalex.org/W3088339160","https://openalex.org/W3098464718","https://openalex.org/W3101985844","https://openalex.org/W3103858635","https://openalex.org/W3103951036","https://openalex.org/W3177525997","https://openalex.org/W4294327662","https://openalex.org/W4301045096"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"Abstract":[0],"Complex":[1],"systems,":[2,36],"represented":[3],"as":[4,37],"dynamic":[5],"networks,":[6],"comprise":[7],"of":[8,25,63,92,102,133],"components":[9],"that":[10,107],"influence":[11],"each":[12],"other":[13],"via":[14],"direct":[15],"and/or":[16],"indirect":[17],"interactions.":[18],"Recent":[19],"research":[20],"has":[21],"shown":[22],"the":[23,38,44,64,127],"importance":[24],"using":[26],"Higher-Order":[27],"Networks":[28],"(HONs)":[29],"for":[30,88,126,136],"modeling":[31,132],"and":[32,84,131],"analyzing":[33],"such":[34],"complex":[35,66,97,134],"typical":[39],"Markovian":[40],"assumption":[41],"in":[42,118],"developing":[43],"First":[45],"Order":[46],"Network":[47],"(FON)":[48],"can":[49],"be":[50],"limiting.":[51],"This":[52],"higher-order":[53,89,109,128],"network":[54,74,90,110,129],"representation":[55,62,91,111,130],"not":[56],"only":[57],"creates":[58],"a":[59,82,96,124],"more":[60,72,116],"accurate":[61,73,85,117],"underlying":[65],"system,":[67],"but":[68],"also":[69],"leads":[70],"to":[71],"analysis.":[75],"In":[76],"this":[77,108],"paper,":[78],"we":[79,105],"first":[80],"present":[81],"scalable":[83],"model,":[86],",":[87],"data":[93],"derived":[94],"from":[95],"system":[98],"with":[99],"various":[100],"orders":[101],"dependencies.":[103],"Then,":[104],"show":[106],"modeled":[112],"by":[113],"is":[114],"significantly":[115],"identifying":[119],"anomalies":[120],"than":[121],"FON,":[122],"demonstrating":[123],"need":[125],"systems":[135],"deriving":[137],"meaningful":[138],"conclusions.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2020-06-19T00:00:00"}