{"id":"https://openalex.org/W4306317295","doi":"https://doi.org/10.1145/3511808.3557350","title":"Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting","display_name":"Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317295","doi":"https://doi.org/10.1145/3511808.3557350"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557350","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3511808.3557350","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3511808.3557350","source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3511808.3557350","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008871638","display_name":"Yihong Ma","orcid":"https://orcid.org/0000-0003-4729-5953"},"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":"Yihong Ma","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024416861","display_name":"Patrick D. Gerard","orcid":"https://orcid.org/0000-0003-3710-6055"},"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":"Patrick Gerard","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057838053","display_name":"Yijun Tian","orcid":"https://orcid.org/0000-0003-2795-6080"},"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":"Yijun Tian","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100932875","display_name":"Zhichun Guo","orcid":null},"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":"Zhichun Guo","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"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":false,"raw_author_name":"Nitesh V. Chawla","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":13.6935,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.99193548,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1481","last_page":"1490"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9943000078201294,"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/computer-science","display_name":"Computer science","score":0.7828614711761475},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6290309429168701},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5070712566375732},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4934300482273102},{"id":"https://openalex.org/keywords/pandemic","display_name":"Pandemic","score":0.4654899835586548},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.44464248418807983},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.43631595373153687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43460285663604736},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3921854496002197},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.3276628255844116}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7828614711761475},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6290309429168701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5070712566375732},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4934300482273102},{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.4654899835586548},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.44464248418807983},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.43631595373153687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43460285663604736},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3921854496002197},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.3276628255844116},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557350","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3511808.3557350","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3511808.3557350","source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3511808.3557350","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3511808.3557350","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3511808.3557350","source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8600000143051147,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4306317295.pdf","grobid_xml":"https://content.openalex.org/works/W4306317295.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2109734180","https://openalex.org/W2131681506","https://openalex.org/W2150593711","https://openalex.org/W2604803309","https://openalex.org/W2798329844","https://openalex.org/W2975753356","https://openalex.org/W3022787740","https://openalex.org/W3045642713","https://openalex.org/W3080422828","https://openalex.org/W3099768174","https://openalex.org/W3102040795","https://openalex.org/W3104302219","https://openalex.org/W3125676075","https://openalex.org/W3139204882","https://openalex.org/W3171958173","https://openalex.org/W3174022889","https://openalex.org/W3175016653"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W2964335273","https://openalex.org/W1889624880","https://openalex.org/W2229372569"],"abstract_inverted_index":{"The":[0,177],"spread":[1],"of":[2,28,43,50,79,142,148,193,218],"COVID-19":[3,51,219],"throughout":[4],"the":[5,13,26,29,41,53,107,187,191],"world":[6],"has":[7,33],"led":[8],"to":[9,21,128,206],"cataclysmic":[10],"consequences":[11],"on":[12,35],"global":[14],"community,":[15],"which":[16,132],"poses":[17],"an":[18,77],"urgent":[19],"need":[20],"accurately":[22],"understand":[23],"and":[24,76,104,136,167,169],"predict":[25],"trajectories":[27],"pandemic.":[30],"Existing":[31],"research":[32],"relied":[34],"graph-structured":[36],"human":[37],"mobility":[38,61,67,113,144],"data":[39],"for":[40,90],"task":[42],"pandemic":[44,48,91,130],"forecasting.":[45],"To":[46,115],"perform":[47,97,129],"forecasting":[49,92],"in":[52,100,111,173],"United":[54],"States,":[55],"we":[56,119,200],"curate":[57],"Large-MG,":[58],"a":[59,101,112,121,140,154,162,174,182,202],"large-scale":[60],"dataset":[62],"that":[63,94,160,185],"contains":[64],"66":[65],"dynamic":[66,143],"graphs,":[68],"with":[69,84,224],"each":[70],"graph":[71,156],"having":[72],"over":[73],"3k":[74],"nodes":[75],"average":[78],"540k":[80],"edges.":[81],"One":[82,152],"drawback":[83],"existing":[85],"Graph":[86,124],"Neural":[87,125],"Networks":[88],"(GNNs)":[89],"is":[93,153,181],"they":[95],"generally":[96],"information":[98,138,171],"propagation":[99,172],"flat":[102],"way":[103],"thus":[105],"ignore":[106],"inherent":[108],"community":[109],"structure":[110],"graph.":[114],"bridge":[116],"this":[117],"gap,":[118],"propose":[120],"Hierarchical":[122],"Spatio-Temporal":[123],"Network":[126],"(HiSTGNN)":[127],"forecasting,":[131],"learns":[133],"both":[134],"spatial":[135],"temporal":[137,188],"from":[139,197],"sequence":[141,192],"graphs.":[145],"HiSTGNN":[146],"consists":[147],"two":[149],"network":[150,158,179],"architectures.":[151],"hierarchical":[155,175],"neural":[157,164],"(HiGNN)":[159],"constructs":[161],"two-level":[163],"architecture:":[165],"county-level":[166],"region-level,":[168],"performs":[170],"way.":[176],"other":[178],"architecture":[180],"Transformer-based":[183],"model":[184],"captures":[186],"dynamics":[189],"among":[190],"learned":[194],"node":[195],"representations":[196],"HiGNN.":[198],"Additionally,":[199],"introduce":[201],"joint":[203],"learning":[204],"objective":[205],"further":[207],"optimize":[208],"HiSTGNN.":[209],"Extensive":[210],"experiments":[211],"have":[212],"demonstrated":[213],"HiSTGNN's":[214],"superior":[215],"predictive":[216],"power":[217],"new":[220],"case/death":[221],"counts":[222],"compared":[223],"state-of-the-art":[225],"baselines.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
