{"id":"https://openalex.org/W4318147862","doi":"https://doi.org/10.1109/bigdata55660.2022.10020863","title":"Graph Structure Neural Differential Equations on Spatio-temporal Prediction","display_name":"Graph Structure Neural Differential Equations on Spatio-temporal Prediction","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147862","doi":"https://doi.org/10.1109/bigdata55660.2022.10020863"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020863","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 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":"2022 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/A5011232118","display_name":"Hangtao He","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hangtao He","raw_affiliation_strings":["Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,China","Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China","University of Chinese Academy of Sciences, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012757772","display_name":"Kejiang Ye","orcid":"https://orcid.org/0000-0001-6133-407X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kejiang Ye","raw_affiliation_strings":["Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,China","Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011232118"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210145761","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":1.0112,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75569106,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1830","last_page":"1835"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.987500011920929,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9871000051498413,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.7284671068191528},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.639447808265686},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.559598445892334},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5450087785720825},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5417661070823669},{"id":"https://openalex.org/keywords/differential-equation","display_name":"Differential equation","score":0.5295665860176086},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4809698164463043},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42503857612609863},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3695918917655945},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27117952704429626},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12066993117332458}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7284671068191528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.639447808265686},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.559598445892334},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5450087785720825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5417661070823669},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.5295665860176086},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4809698164463043},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42503857612609863},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3695918917655945},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27117952704429626},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12066993117332458},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020863","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 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":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335892","display_name":"Youth Innovation Promotion Association","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1924770834","https://openalex.org/W2033825305","https://openalex.org/W2065316639","https://openalex.org/W2157331557","https://openalex.org/W2302255633","https://openalex.org/W2302618556","https://openalex.org/W2600297185","https://openalex.org/W2808409763","https://openalex.org/W2911286998","https://openalex.org/W2952734551","https://openalex.org/W2963755523","https://openalex.org/W2964015378","https://openalex.org/W2996847713","https://openalex.org/W3025713978","https://openalex.org/W3027983943","https://openalex.org/W3091014027","https://openalex.org/W3115783604","https://openalex.org/W3152893301","https://openalex.org/W3157992795","https://openalex.org/W3163837885","https://openalex.org/W3174804935","https://openalex.org/W3213170289","https://openalex.org/W4205787683","https://openalex.org/W4226047355","https://openalex.org/W4226061867","https://openalex.org/W4231591459","https://openalex.org/W4238209390","https://openalex.org/W4287167215","https://openalex.org/W4288093153","https://openalex.org/W4297733535","https://openalex.org/W4298224527","https://openalex.org/W4385245566","https://openalex.org/W6640212811","https://openalex.org/W6726873649","https://openalex.org/W6745537798","https://openalex.org/W6745764291","https://openalex.org/W6752307458","https://openalex.org/W6760045743","https://openalex.org/W6768936351","https://openalex.org/W6771612237","https://openalex.org/W6778353478","https://openalex.org/W6795423661","https://openalex.org/W6797363331","https://openalex.org/W6810971026"],"related_works":["https://openalex.org/W1492103595","https://openalex.org/W2364741597","https://openalex.org/W1864774435","https://openalex.org/W946352265","https://openalex.org/W3020787026","https://openalex.org/W2334479858","https://openalex.org/W2799209613","https://openalex.org/W1971388572","https://openalex.org/W1507702947","https://openalex.org/W2370926798"],"abstract_inverted_index":{"Deep":[0],"learning":[1,16,55],"is":[2,37,44,92],"one":[3],"of":[4,77,95,114,148],"the":[5,35,75,111,146,149,159],"most":[6],"widely":[7],"used":[8,136],"modeling":[9],"approach":[10],"for":[11,53,131],"spatio-temporal":[12,115,132],"prediction.":[13,133],"Traditional":[14],"deep":[15,54],"models":[17],"get":[18],"discrete":[19,25],"features":[20,49],"or":[21,40],"hidden":[22,51,103],"states":[23,52],"from":[24],"data.":[26,89],"However":[27],"such":[28],"discretization":[29],"method":[30],"cannot":[31],"work":[32],"well":[33],"if":[34],"data":[36],"irregularly":[38],"sampled":[39],"incompletely":[41],"observed.":[42],"It":[43],"difficult":[45],"to":[46,63,68,84,126,140,144,164],"obtain":[47,101,110],"continuous":[48,102],"and":[50],"models.":[56],"In":[57],"this":[58,70],"paper,":[59],"we":[60,117,135],"demonstrate":[61],"how":[62],"use":[64],"neural":[65,96,137],"differential":[66,97,138],"equations":[67,139],"solve":[69],"problem.":[71],"We":[72],"first":[73],"validated":[74],"ability":[76],"Neural":[78,90,107,124],"Controlled":[79],"Differential":[80],"Equations":[81],"(Neural":[82],"CDE)":[83],"process":[85],"real-world":[86],"irregular":[87],"spatiotemporal":[88],"CDE":[91,108,125],"a":[93,128,142],"type":[94],"equation":[98],"that":[99],"can":[100,109],"states.":[104],"To":[105],"make":[106],"spatial":[112],"correlation":[113],"data,":[116],"combined":[118],"Graph":[119],"Attention":[120],"Network":[121],"(GAT)":[122],"with":[123],"propose":[127],"new":[129],"model":[130,157],"Furthermore,":[134],"designed":[141],"module":[143],"reduce":[145],"error":[147],"prediction":[150],"results.":[151],"Experimental":[152],"results":[153],"show":[154],"our":[155],"proposed":[156],"outperforms":[158],"standard":[160],"baseline":[161],"by":[162],"up":[163],"37.40%.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
