{"id":"https://openalex.org/W4412876973","doi":"https://doi.org/10.1145/3711896.3737116","title":"Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation","display_name":"Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412876973","doi":"https://doi.org/10.1145/3711896.3737116"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737116","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737116","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737116","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737116","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040630874","display_name":"Zhaoyang Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoyang Zhang","raw_affiliation_strings":["School of Statistics, East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0005-5801-4557","affiliations":[{"raw_affiliation_string":"School of Statistics, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100715922","display_name":"Ziqi Chen","orcid":"https://orcid.org/0000-0002-4128-2986"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqi Chen","raw_affiliation_strings":["School of Statistics, East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-4128-2986","affiliations":[{"raw_affiliation_string":"School of Statistics, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393711","display_name":"Qiao Liu","orcid":"https://orcid.org/0000-0002-9781-3360"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiao Liu","raw_affiliation_strings":["Department of Statistics, Stanford University, Stanford, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-9781-3360","affiliations":[{"raw_affiliation_string":"Department of Statistics, Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101055390","display_name":"Jinhan Xie","orcid":"https://orcid.org/0000-0001-7407-8474"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhan Xie","raw_affiliation_strings":["Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, Yunnan, China"],"raw_orcid":"https://orcid.org/0000-0001-7407-8474","affiliations":[{"raw_affiliation_string":"Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077961759","display_name":"Hongtu Zhu","orcid":"https://orcid.org/0000-0002-6781-2690"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongtu Zhu","raw_affiliation_strings":["Department of Biostatistics, Statistics, Computer Science and Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"],"raw_orcid":"https://orcid.org/0000-0002-6781-2690","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Statistics, Computer Science and Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3912","last_page":"3920"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9962000250816345,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7538000345230103},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6765284538269043},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5362033247947693},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.49921679496765137},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4722185432910919},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4334743618965149},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41192081570625305},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39061272144317627},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30017751455307007},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.2765346169471741},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19909119606018066},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07268500328063965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7538000345230103},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6765284538269043},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5362033247947693},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.49921679496765137},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4722185432910919},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4334743618965149},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41192081570625305},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39061272144317627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30017751455307007},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2765346169471741},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19909119606018066},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07268500328063965},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3737116","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737116","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737116","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737116","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737116","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737116","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2405216287","display_name":null,"funder_award_id":"12271167","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6096554537","display_name":null,"funder_award_id":"22JC1400800","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"},{"id":"https://openalex.org/G6807970762","display_name":null,"funder_award_id":"72331005","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"},{"id":"https://openalex.org/G8316206084","display_name":null,"funder_award_id":"72331005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412876973.pdf","grobid_xml":"https://content.openalex.org/works/W4412876973.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1736726159","https://openalex.org/W2008599619","https://openalex.org/W2068456795","https://openalex.org/W2115098571","https://openalex.org/W2771817472","https://openalex.org/W2966916615","https://openalex.org/W2998496395","https://openalex.org/W3033108443","https://openalex.org/W3092903641","https://openalex.org/W3094678421","https://openalex.org/W3106279603","https://openalex.org/W3133971262","https://openalex.org/W3208378893","https://openalex.org/W4225606062","https://openalex.org/W4235111205","https://openalex.org/W4382202969","https://openalex.org/W4382203079","https://openalex.org/W4383374342","https://openalex.org/W4383621844","https://openalex.org/W4392367395","https://openalex.org/W6802703248"],"related_works":["https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W1997992934","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W2125114371","https://openalex.org/W2019977573","https://openalex.org/W2149980199","https://openalex.org/W3125766170","https://openalex.org/W4226363941"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,91],"novel":[6],"framework,":[7],"the":[8,18,120,156],"Sampling-guided":[9],"Heterogeneous":[10],"Graph":[11],"Neural":[12],"Network":[13],"(HT-GNN),":[14],"to":[15,35,99],"effectively":[16,103],"tackle":[17],"challenge":[19],"of":[20,158],"missing":[21,40,46,109,138],"data":[22,47,139],"imputation":[23,133,149],"in":[24,155],"longitudinal":[25,72,161],"studies.":[26],"Unlike":[27],"traditional":[28],"methods,":[29,134],"which":[30],"often":[31],"require":[32],"extensive":[33],"preprocessing":[34],"handle":[36],"irregular":[37],"or":[38],"inconsistent":[39],"data,":[41],"our":[42],"approach":[43],"accommodates":[44],"arbitrary":[45],"patterns":[48],"while":[49,74,102],"maintaining":[50],"computational":[51],"efficiency.":[52],"HT-GNN":[53,96,129],"models":[54],"both":[55,114],"observations":[56],"and":[57,90,107,116,151],"covariates":[58],"as":[59],"distinct":[60],"node":[61,105],"types,":[62],"connecting":[63],"observation":[64],"nodes":[65],"at":[66],"successive":[67],"time":[68],"points":[69],"through":[70],"subject-specific":[71],"subnetworks,":[73],"covariate-observation":[75],"interactions":[76],"are":[77],"represented":[78],"by":[79],"attributed":[80],"edges":[81],"within":[82],"bipartite":[83],"graphs.":[84],"By":[85],"leveraging":[86],"subject-wise":[87],"mini-batch":[88],"sampling":[89],"multi-layer":[92],"temporal":[93],"smoothing":[94],"mechanism,":[95],"efficiently":[97],"scales":[98],"large":[100],"datasets,":[101,118],"learning":[104],"representations":[106],"imputing":[108],"data.":[110,162],"Extensive":[111],"experiments":[112],"on":[113],"synthetic":[115],"real-world":[117],"including":[119],"Alzheimer's":[121],"Disease":[122],"Neuroimaging":[123],"Initiative":[124],"(DNI)":[125],"dataset,":[126],"demonstrate":[127],"that":[128],"significantly":[130],"outperforms":[131],"existing":[132],"even":[135],"with":[136],"high":[137],"rates":[140],"(e.g.,":[141],"80%).":[142],"The":[143],"empirical":[144],"results":[145],"highlight":[146],"HT-GNN's":[147],"robust":[148],"capabilities":[150],"superior":[152],"performance,":[153],"particularly":[154],"context":[157],"complex,":[159],"large-scale":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}