{"id":"https://openalex.org/W4205484491","doi":"https://doi.org/10.1109/bigdata52589.2021.9671810","title":"Characterizing Disease Spreading via Visibility Graph Embedding","display_name":"Characterizing Disease Spreading via Visibility Graph Embedding","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205484491","doi":"https://doi.org/10.1109/bigdata52589.2021.9671810"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671810","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/A5110179590","display_name":"Kang-Yu Ni","orcid":null},"institutions":[{"id":"https://openalex.org/I200576644","display_name":"HRL Laboratories (United States)","ror":"https://ror.org/05p7te762","country_code":"US","type":"company","lineage":["https://openalex.org/I200576644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kangyu Ni","raw_affiliation_strings":["HRL Laboratories, Malibu, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HRL Laboratories, Malibu, CA, USA","institution_ids":["https://openalex.org/I200576644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102267796","display_name":"Jiejun Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I200576644","display_name":"HRL Laboratories (United States)","ror":"https://ror.org/05p7te762","country_code":"US","type":"company","lineage":["https://openalex.org/I200576644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiejun Xu","raw_affiliation_strings":["HRL Laboratories, Malibu, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HRL Laboratories, Malibu, CA, USA","institution_ids":["https://openalex.org/I200576644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091137947","display_name":"Shane Roach","orcid":null},"institutions":[{"id":"https://openalex.org/I200576644","display_name":"HRL Laboratories (United States)","ror":"https://ror.org/05p7te762","country_code":"US","type":"company","lineage":["https://openalex.org/I200576644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shane Roach","raw_affiliation_strings":["HRL Laboratories, Malibu, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HRL Laboratories, Malibu, CA, USA","institution_ids":["https://openalex.org/I200576644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068965681","display_name":"Tsai-Ching Lu","orcid":"https://orcid.org/0000-0003-3936-8446"},"institutions":[{"id":"https://openalex.org/I200576644","display_name":"HRL Laboratories (United States)","ror":"https://ror.org/05p7te762","country_code":"US","type":"company","lineage":["https://openalex.org/I200576644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tsai-Ching Lu","raw_affiliation_strings":["HRL Laboratories, Malibu, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HRL Laboratories, Malibu, CA, USA","institution_ids":["https://openalex.org/I200576644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089905338","display_name":"Alexei Kopylov","orcid":null},"institutions":[{"id":"https://openalex.org/I200576644","display_name":"HRL Laboratories (United States)","ror":"https://ror.org/05p7te762","country_code":"US","type":"company","lineage":["https://openalex.org/I200576644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexei Kopylov","raw_affiliation_strings":["HRL Laboratories, Malibu, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HRL Laboratories, Malibu, CA, USA","institution_ids":["https://openalex.org/I200576644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1818854,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2656","last_page":"2661"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973000288009644,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973000288009644,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9957000017166138,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/visibility","display_name":"Visibility","score":0.7247709631919861},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6651219129562378},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5942137241363525},{"id":"https://openalex.org/keywords/visibility-graph","display_name":"Visibility graph","score":0.5911960005760193},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.513498842716217},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4119030237197876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3773242235183716},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27550894021987915},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17288541793823242},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09093040227890015},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.08236372470855713},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.06528410315513611}],"concepts":[{"id":"https://openalex.org/C123403432","wikidata":"https://www.wikidata.org/wiki/Q654068","display_name":"Visibility","level":2,"score":0.7247709631919861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6651219129562378},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5942137241363525},{"id":"https://openalex.org/C173362246","wikidata":"https://www.wikidata.org/wiki/Q8216024","display_name":"Visibility graph","level":3,"score":0.5911960005760193},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.513498842716217},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4119030237197876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3773242235183716},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27550894021987915},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17288541793823242},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09093040227890015},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.08236372470855713},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.06528410315513611},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671810","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":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.7900000214576721}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332467","display_name":"U.S. Air Force","ror":"https://ror.org/006gmme17"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1557624102","https://openalex.org/W2055538060","https://openalex.org/W2187089797","https://openalex.org/W2737925311","https://openalex.org/W2750821778","https://openalex.org/W2804381853","https://openalex.org/W2897218018","https://openalex.org/W2963224980","https://openalex.org/W2963768879","https://openalex.org/W2995509183","https://openalex.org/W3008443627","https://openalex.org/W3012864042","https://openalex.org/W3014687713","https://openalex.org/W3022787740","https://openalex.org/W3023598638","https://openalex.org/W3040134252","https://openalex.org/W3044973699","https://openalex.org/W3106039696","https://openalex.org/W3124315549","https://openalex.org/W4205386763","https://openalex.org/W4289422895","https://openalex.org/W6633389698","https://openalex.org/W6677886600","https://openalex.org/W6741450815","https://openalex.org/W6743487926","https://openalex.org/W6775763934","https://openalex.org/W6777443043","https://openalex.org/W6780854277"],"related_works":["https://openalex.org/W2743652671","https://openalex.org/W3188793535","https://openalex.org/W2032709970","https://openalex.org/W2066813664","https://openalex.org/W2094166299","https://openalex.org/W2519024191","https://openalex.org/W2963183586","https://openalex.org/W2159176266","https://openalex.org/W3036264823","https://openalex.org/W3206528106"],"abstract_inverted_index":{"Gaining":[0],"timely":[1],"insights":[2,208],"on":[3,54,193],"real-world":[4],"emergency":[5],"events,":[6],"such":[7],"as":[8,49,138],"infectious":[9,214],"disease":[10],"outbreaks,":[11],"is":[12,136],"critical":[13],"for":[14,51],"developing":[15],"appropriate":[16,178],"response":[17,179],"strategies.":[18],"In":[19],"this":[20],"work,":[21],"we":[22,38],"propose":[23],"a":[24,42,55,123,139,143,156],"data-driven":[25],"approach":[26,91,160,203],"to":[27,40,110],"study":[28],"the":[29,33,61,102,163,174,194,211],"spreading":[30,212],"dynamics":[31],"of":[32,44,63,67,71,81,213],"global":[34],"Covid-19":[35],"pandemic.":[36],"Specifically,":[37],"aim":[39],"identify":[41,162],"set":[43],"most":[45,164],"\u201csimilar\u201d":[46],"geographic":[47,166],"regions":[48,167],"proxies":[50],"making":[52],"predictions":[53,59,74],"targeted":[56,157],"location.":[57],"Example":[58],"include":[60],"number":[62,66,70],"new":[64],"cases,":[65],"hospitalizations,":[68],"and":[69,87,115,181],"deaths.":[72],"Such":[73],"can":[75,161,189],"be":[76,190],"made":[77],"at":[78],"different":[79],"levels":[80],"regional":[82,95],"granularities,":[83],"including":[84],"city,":[85],"county,":[86],"state":[88],"levels.":[89],"Our":[90,202],"starts":[92],"by":[93,168],"transforming":[94],"time":[96,134,153],"series":[97,135],"into":[98],"graph":[99,105],"representations":[100],"using":[101,127],"natural":[103],"visibility":[104],"(NVG)":[106],"model":[107],"in":[108,142,173,209],"order":[109],"capture":[111],"their":[112],"intrinsic":[113],"trends":[114],"properties.":[116],"These":[117],"graphs":[118],"are":[119],"then":[120],"projected":[121],"onto":[122],"common":[124],"embedding":[125,130,145,175],"space":[126],"graph-level":[128],"network":[129],"techniques.":[131],"Essentially,":[132],"each":[133],"converted":[137],"data":[140],"point":[141],"feature":[144],"space,":[146],"where":[147],"spatial":[148],"proximity":[149],"indicates":[150],"similarity":[151],"among":[152],"series.":[154],"Given":[155],"region,":[158],"our":[159],"\u201crelevant\u201d":[165],"finding":[169],"its":[170],"k-nearest":[171],"neighbors":[172],"space.":[176],"Subsequently,":[177],"strategies":[180],"policies":[182],"(e.g.,":[183],"school":[184],"shutdown,":[185],"indoor":[186],"dining":[187],"restriction)":[188],"adapted":[191],"based":[192],"success":[195],"or":[196],"failure":[197],"experiences":[198],"from":[199],"relevant":[200],"regions.":[201],"will":[204],"potentially":[205],"provide":[206],"valuable":[207],"mitigating":[210],"disease.":[215]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}