{"id":"https://openalex.org/W4378942662","doi":"https://doi.org/10.1145/3580305.3599444","title":"Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders","display_name":"Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4378942662","doi":"https://doi.org/10.1145/3580305.3599444","pmid":"https://pubmed.ncbi.nlm.nih.gov/40276024"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599444","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599444","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599444","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599444","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054680457","display_name":"Dingsu Wang","orcid":"https://orcid.org/0000-0003-0395-3280"},"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":"Dingsu Wang","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100634104","display_name":"Yuchen Yan","orcid":"https://orcid.org/0000-0001-9785-5352"},"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":false,"raw_author_name":"Yuchen Yan","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028817466","display_name":"Ruizhong Qiu","orcid":"https://orcid.org/0009-0000-3253-8890"},"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":false,"raw_author_name":"Ruizhong Qiu","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101792548","display_name":"Yada Zhu","orcid":"https://orcid.org/0000-0002-3338-6371"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yada Zhu","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053935458","display_name":"Kaiyu Guan","orcid":"https://orcid.org/0000-0002-3499-6382"},"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":false,"raw_author_name":"Kaiyu Guan","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033548666","display_name":"Andrew J. Margenot","orcid":"https://orcid.org/0000-0003-0185-8650"},"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":false,"raw_author_name":"Andrew Margenot","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068043486","display_name":"Hanghang Tong","orcid":"https://orcid.org/0000-0003-4405-3887"},"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":false,"raw_author_name":"Hanghang Tong","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5054680457"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":4.4935,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.95622938,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"2023","issue":null,"first_page":"2256","last_page":"2268"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9995999932289124,"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":0.9995999932289124,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9919999837875366,"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":0.7205374836921692},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7072743773460388},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7000972628593445},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5554510354995728},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5447729825973511},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.44480544328689575},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4436204433441162},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4140043258666992},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36986014246940613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3594818413257599},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3289335072040558},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2789596915245056}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7205374836921692},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7072743773460388},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7000972628593445},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5554510354995728},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5447729825973511},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44480544328689575},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4436204433441162},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4140043258666992},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36986014246940613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3594818413257599},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3289335072040558},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2789596915245056}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3580305.3599444","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599444","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599444","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmid:40276024","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40276024","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":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","raw_type":null},{"id":"pmh:oai:arXiv.org:2305.18612","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.18612","pdf_url":"https://arxiv.org/pdf/2305.18612","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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pubmedcentral.nih.gov:12019811","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12019811","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12019811/pdf/nihms-2073489.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":"KDD","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599444","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599444","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599444","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1273594594","display_name":null,"funder_award_id":"17STQAC00001-06-00","funder_id":"https://openalex.org/F4320306110","funder_display_name":"U.S. Department of Homeland Security"},{"id":"https://openalex.org/G1490481123","display_name":null,"funder_award_id":"1939725","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2540003204","display_name":null,"funder_award_id":"194713","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2741821012","display_name":null,"funder_award_id":"947135","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2926720356","display_name":null,"funder_award_id":"32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G3748775989","display_name":null,"funder_award_id":"HR001121C0165","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4403888875","display_name":null,"funder_award_id":"W911NF2110088","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G5524522455","display_name":null,"funder_award_id":"DARPA","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G5620962805","display_name":null,"funder_award_id":"67021","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G5838847886","display_name":null,"funder_award_id":"1947135","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6044938303","display_name":null,"funder_award_id":"1947135,2134079,1939725","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6770826516","display_name":null,"funder_award_id":"2020-67021-32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G7091742882","display_name":null,"funder_award_id":"HR001121C0165","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8998121839","display_name":null,"funder_award_id":"911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G984405641","display_name":null,"funder_award_id":"2134079","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306110","display_name":"U.S. Department of Homeland Security","ror":"https://ror.org/00jyr0d86"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378942662.pdf","grobid_xml":"https://content.openalex.org/works/W4378942662.grobid-xml"},"referenced_works_count":88,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1606533552","https://openalex.org/W1686946872","https://openalex.org/W1924770834","https://openalex.org/W1969865391","https://openalex.org/W1983883318","https://openalex.org/W2024760831","https://openalex.org/W2101491865","https://openalex.org/W2133299088","https://openalex.org/W2134843796","https://openalex.org/W2139708568","https://openalex.org/W2146705998","https://openalex.org/W2294076426","https://openalex.org/W2296438605","https://openalex.org/W2408316103","https://openalex.org/W2467604901","https://openalex.org/W2469618837","https://openalex.org/W2531563875","https://openalex.org/W2606780347","https://openalex.org/W2753738274","https://openalex.org/W2803403013","https://openalex.org/W2803805253","https://openalex.org/W2807692250","https://openalex.org/W2890686416","https://openalex.org/W2908804015","https://openalex.org/W2952575904","https://openalex.org/W2962711740","https://openalex.org/W2963263347","https://openalex.org/W2963358464","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964425131","https://openalex.org/W2969280827","https://openalex.org/W2991079997","https://openalex.org/W2998436408","https://openalex.org/W3007404067","https://openalex.org/W3012816161","https://openalex.org/W3033529678","https://openalex.org/W3034285813","https://openalex.org/W3035623224","https://openalex.org/W3036981181","https://openalex.org/W3038787377","https://openalex.org/W3080253043","https://openalex.org/W3081102048","https://openalex.org/W3085139254","https://openalex.org/W3090016979","https://openalex.org/W3090999459","https://openalex.org/W3094003325","https://openalex.org/W3097986917","https://openalex.org/W3103720336","https://openalex.org/W3105978366","https://openalex.org/W3108054588","https://openalex.org/W3129178271","https://openalex.org/W3130828726","https://openalex.org/W3132992987","https://openalex.org/W3154679372","https://openalex.org/W3155683369","https://openalex.org/W3156302073","https://openalex.org/W3156855620","https://openalex.org/W3163053496","https://openalex.org/W3174697924","https://openalex.org/W3175110359","https://openalex.org/W3177317072","https://openalex.org/W3208238874","https://openalex.org/W3208499031","https://openalex.org/W4221147848","https://openalex.org/W4221150249","https://openalex.org/W4281658119","https://openalex.org/W4285797041","https://openalex.org/W4286892599","https://openalex.org/W4286902310","https://openalex.org/W4287062502","https://openalex.org/W4287755062","https://openalex.org/W4288283362","https://openalex.org/W4288284685","https://openalex.org/W4290877199","https://openalex.org/W4293469690","https://openalex.org/W4295312788","https://openalex.org/W4295838474","https://openalex.org/W4297947878","https://openalex.org/W4303438998","https://openalex.org/W4306317262","https://openalex.org/W4319335604","https://openalex.org/W4320013936","https://openalex.org/W4322614756","https://openalex.org/W4385245566","https://openalex.org/W4391602018","https://openalex.org/W6636206708"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W2903115227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,33,99,142,168,226],"series":[2,34,143,169,227],"(MTS)":[3],"imputation":[4,90,132],"is":[5,74,106],"a":[6,119,151,178,209],"widely":[7],"studied":[8],"problem":[9,130],"in":[10,91,139,190,224],"recent":[11],"years.":[12],"Existing":[13],"methods":[14,64],"can":[15],"be":[16],"divided":[17],"into":[18],"two":[19],"main":[20],"groups,":[21],"including":[22],"(1)":[23],"deep":[24],"recurrent":[25],"or":[26,69],"generative":[27],"models":[28,43],"that":[29,44],"primarily":[30],"focus":[31],"on":[32,184],"features,":[35],"and":[36,76,109,145,171,228],"(2)":[37],"graph":[38,52,72,86,105,146,172,202,229],"neural":[39,203],"networks":[40,204],"(GNNs)":[41],"based":[42,183,201],"utilize":[45,84],"the":[46,50,71,85,103,129,191,215,235],"topological":[47,67],"information":[48,68],"from":[49,214],"inherent":[51],"structure":[53,73],"of":[54,131,217,237],"MTS":[55,94],"as":[56,97],"relational":[57],"inductive":[58],"bias":[59],"for":[60,88],"imputation.":[61],"Nevertheless,":[62],"these":[63,124],"either":[65],"neglect":[66],"assume":[70],"fixed":[75],"accurately":[77],"known.":[78],"Thus,":[79],"they":[80],"fail":[81],"to":[82,122,161,220],"fully":[83],"dynamics":[87],"precise":[89],"more":[92],"challenging":[93],"data":[95],"such":[96],"<i>networked":[98],"series</i>":[100],"(<i>NTS</i>),":[101],"where":[102],"underlying":[104],"constantly":[107],"changing":[108],"might":[110],"have":[111],"missing":[112,137,163,222],"edges.":[113],"In":[114,174],"this":[115],"paper,":[116],"we":[117,127,149,176],"propose":[118,177],"novel":[120],"approach":[121],"overcome":[123],"limitations.":[125],"First,":[126],"define":[128],"over":[133,165,240],"NTS":[134],"which":[135,156],"contains":[136],"values":[138,164,223],"both":[140,166,225],"node":[141,167,180],"features":[144,170],"structures.":[147,173],"Then,":[148],"design":[150,208],"new":[152,179],"model":[153,239],"named":[154],"PoGeVon":[155],"leverages":[157],"variational":[158],"autoencoder":[159],"(VAE)":[160],"predict":[162],"particular,":[175],"position":[181],"embedding":[182],"random":[185],"walk":[186],"with":[187,193,199,211],"restart":[188],"(RWR)":[189],"encoder":[192],"provable":[194],"higher":[195],"expressive":[196],"power":[197],"compared":[198],"message-passing":[200],"(GNNs).":[205],"We":[206],"further":[207],"decoder":[210],"3-stage":[212],"predictions":[213],"perspective":[216],"multi-task":[218],"learning":[219],"impute":[221],"structures":[230],"reciprocally.":[231],"Experiment":[232],"results":[233],"demonstrate":[234],"effectiveness":[236],"our":[238],"baselines.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":13}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
