{"id":"https://openalex.org/W4392971690","doi":"https://doi.org/10.1145/3627673.3679642","title":"Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation","display_name":"Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4392971690","doi":"https://doi.org/10.1145/3627673.3679642","pmid":"https://pubmed.ncbi.nlm.nih.gov/40041384"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679642","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679642","pdf_url":null,"source":null,"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 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3627673.3679642","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077678202","display_name":"Baoyu Jing","orcid":"https://orcid.org/0000-0003-1564-6499"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Baoyu Jing","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dawei Zhou","orcid":"https://orcid.org/0000-0002-7065-2990"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dawei Zhou","raw_affiliation_strings":["Virginia Polytechnic Institute and State University, Blacksburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"Virginia Polytechnic Institute and State University, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102807475","display_name":"Kan Ren","orcid":"https://orcid.org/0000-0002-4032-9615"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kan Ren","raw_affiliation_strings":["Microsoft Research Asia, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Shanghai, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":null,"display_name":"Carl Yang","orcid":"https://orcid.org/0000-0001-9145-4531"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carl Yang","raw_affiliation_strings":["Emory University, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077678202"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.9443,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.86651451,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"2024","issue":null,"first_page":"1027","last_page":"1037"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9677000045776367,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.9677000045776367,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.944599986076355,"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/T11106","display_name":"Data Management and Algorithms","score":0.9240000247955322,"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":0.6435944437980652},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5092455148696899},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5073723196983337},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4953957796096802},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48352575302124023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4748568534851074},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.45451992750167847},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44235560297966003},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41677623987197876},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34844720363616943},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2869102954864502},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.12925416231155396},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09133821725845337}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6435944437980652},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5092455148696899},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5073723196983337},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4953957796096802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48352575302124023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4748568534851074},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.45451992750167847},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44235560297966003},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41677623987197876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34844720363616943},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2869102954864502},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.12925416231155396},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09133821725845337},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1145/3627673.3679642","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679642","pdf_url":null,"source":null,"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 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmid:40041384","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40041384","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","raw_type":null},{"id":"pmh:oai:arXiv.org:2403.11960","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.11960","pdf_url":"https://arxiv.org/pdf/2403.11960","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/121535","is_oa":true,"landing_page_url":"https://hdl.handle.net/10919/121535","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/a8fa17f1-d10b-44b9-aaab-ea4bbf883072/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"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":null,"raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:11876796","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11876796","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11876796/pdf/nihms-2058364.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Proc ACM Int Conf Inf Knowl Manag","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1145/3627673.3679642","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679642","pdf_url":null,"source":null,"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 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1516659296","https://openalex.org/W1969865391","https://openalex.org/W2081028405","https://openalex.org/W2143076232","https://openalex.org/W2172150264","https://openalex.org/W2194775991","https://openalex.org/W2767949765","https://openalex.org/W2962858109","https://openalex.org/W2963214893","https://openalex.org/W2963367478","https://openalex.org/W2964425131","https://openalex.org/W3035103424","https://openalex.org/W3080902222","https://openalex.org/W3094452565","https://openalex.org/W3098168339","https://openalex.org/W3130828726","https://openalex.org/W3132992987","https://openalex.org/W3156855620","https://openalex.org/W3165312697","https://openalex.org/W3171353004","https://openalex.org/W3174697924","https://openalex.org/W3175294706","https://openalex.org/W3176716813","https://openalex.org/W3177266098","https://openalex.org/W3177317072","https://openalex.org/W3181975995","https://openalex.org/W3193631128","https://openalex.org/W3198534342","https://openalex.org/W3209186881","https://openalex.org/W4221139060","https://openalex.org/W4281658119","https://openalex.org/W4285794683","https://openalex.org/W4290943920","https://openalex.org/W4290948450","https://openalex.org/W4292423649","https://openalex.org/W4306317262","https://openalex.org/W4306795182","https://openalex.org/W4312651322","https://openalex.org/W4312863243","https://openalex.org/W4367046806","https://openalex.org/W4378942662","https://openalex.org/W4384816610","https://openalex.org/W4393153112","https://openalex.org/W4393160571","https://openalex.org/W4396723397","https://openalex.org/W4396758707","https://openalex.org/W6791033905"],"related_works":["https://openalex.org/W4211215373","https://openalex.org/W3217094455","https://openalex.org/W2081494945","https://openalex.org/W2389053294","https://openalex.org/W2989589450","https://openalex.org/W3119637569","https://openalex.org/W1970893504","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Spatiotemporal":[0,147,162],"time":[1,37,77,117],"series":[2,78,118],"are":[3,71],"usually":[4,14],"collected":[5],"via":[6,131],"monitoring":[7],"sensors":[8],"placed":[9],"at":[10],"different":[11],"locations,":[12],"which":[13,152],"contain":[15],"missing":[16,31],"values":[17,32,194],"due":[18],"to":[19,53,127],"various":[20],"failures,":[21],"such":[22],"as":[23],"mechanical":[24],"damages":[25],"and":[26,79,94,101,124,160,173,204,215],"Internet":[27],"outages.":[28],"Imputing":[29],"the":[30,50,58,84,99,129,132,137,169,177,193,205,213,219],"is":[33,65],"crucial":[34],"for":[35],"analyzing":[36],"series.":[38],"When":[39],"recovering":[40],"a":[41,121,144,154,161],"specific":[42],"data":[43,62],"point,":[44],"most":[45],"existing":[46],"methods":[47],"consider":[48],"all":[49],"information":[51],"relevant":[52],"that":[54,67,186,209],"point":[55],"regardless":[56],"of":[57,139,171,195],"cause-and-effect":[59],"relationship.":[60],"During":[61],"collection,":[63],"it":[64],"inevitable":[66],"some":[68],"unknown":[69],"confounders":[70,89,130,172],"included,":[72],"e.g.,":[73],"background":[74],"noise":[75],"in":[76,83],"non-causal":[80,96,105],"shortcut":[81],"edges":[82],"constructed":[85],"sensor":[86],"network.":[87],"These":[88],"could":[90,107,167,175,211,216],"open":[91],"backdoor":[92],"paths":[93],"establish":[95],"correlations":[97,106],"between":[98],"input":[100],"output.":[102],"Over-exploiting":[103],"these":[104],"cause":[108],"overfitting.":[109],"In":[110],"this":[111],"paper,":[112],"we":[113,142],"first":[114],"revisit":[115],"spatiotemporal":[116],"imputation":[119],"from":[120],"causal":[122,179,189,220],"perspective":[123],"show":[125,208],"how":[126],"block":[128],"frontdoor":[133,140],"adjustment.":[134],"Based":[135,157],"on":[136,192,200],"results":[138,207],"adjustment,":[141],"introduce":[143],"novel":[145,155],"Causality-Aware":[146],"Graph":[148],"Neural":[149],"Network":[150],"(Casper),":[151],"contains":[153],"Prompt":[156],"Decoder":[158],"(PBD)":[159],"Causal":[163],"Attention":[164],"(SCA).":[165],"PBD":[166],"reduce":[168],"impact":[170],"SCA":[174,187],"discover":[176,218],"sparse":[178],"relationships":[180,190],"among":[181],"embeddings.":[182],"Theoretical":[183],"analysis":[184],"reveals":[185],"discovers":[188],"based":[191],"gradients.":[196],"We":[197],"evaluate":[198],"Casper":[199,210],"three":[201],"real-world":[202],"datasets,":[203],"experimental":[206],"outperform":[212],"baselines":[214],"effectively":[217],"relationships.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}