{"id":"https://openalex.org/W4416460647","doi":"https://doi.org/10.1145/3777547","title":"Enhancing Spatial-Temporal Prediction Models with Dynamic Causal Graphs","display_name":"Enhancing Spatial-Temporal Prediction Models with Dynamic Causal Graphs","publication_year":2025,"publication_date":"2025-11-21","ids":{"openalex":"https://openalex.org/W4416460647","doi":"https://doi.org/10.1145/3777547"},"language":"en","primary_location":{"id":"doi:10.1145/3777547","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3777547","pdf_url":null,"source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-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/A5037507957","display_name":"Yicheng Pan","orcid":"https://orcid.org/0000-0003-4139-1477"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"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":"Yicheng Pan","raw_affiliation_strings":["Peking University","Shenzhen Institute of Advanced Technology Chinese Academy of Sciences","Peking University, Beijing, China","Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-4139-1477","affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haowei Wang","orcid":"https://orcid.org/0009-0007-8036-7636"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haowei Wang","raw_affiliation_strings":["Peking University","Peking University, Beijing China"],"raw_orcid":"https://orcid.org/0009-0007-8036-7636","affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, Beijing China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100762316","display_name":"Meng Ma","orcid":"https://orcid.org/0000-0002-1963-2513"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Ma","raw_affiliation_strings":["Peking University","Peking University, Beijing China"],"raw_orcid":"https://orcid.org/0000-0002-1963-2513","affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, Beijing China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100338689","display_name":"Ping Wang","orcid":"https://orcid.org/0000-0002-8854-2079"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Wang","raw_affiliation_strings":["Peking University","Peking University, Beijing China"],"raw_orcid":"https://orcid.org/0000-0002-8854-2079","affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, Beijing China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27773835,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"4","first_page":"1","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9562000036239624,"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":0.9562000036239624,"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":0.01140000019222498,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0052999998442828655,"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/adjacency-matrix","display_name":"Adjacency matrix","score":0.6725000143051147},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5846999883651733},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5109999775886536},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4763000011444092},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44440001249313354},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4041999876499176},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.35429999232292175},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.35409998893737793}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7562000155448914},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.6725000143051147},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5846999883651733},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48159998655319214},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45419999957084656},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4120999872684479},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.35429999232292175},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3034000098705292},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3777547","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3777547","pdf_url":null,"source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4907620412","display_name":null,"funder_award_id":"92167104, 62072006","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W170458018","https://openalex.org/W1967444754","https://openalex.org/W1973943669","https://openalex.org/W2064675550","https://openalex.org/W2079656335","https://openalex.org/W2110485445","https://openalex.org/W2147800946","https://openalex.org/W2157331557","https://openalex.org/W2166901389","https://openalex.org/W2171234954","https://openalex.org/W2528639018","https://openalex.org/W2604847698","https://openalex.org/W2623865294","https://openalex.org/W2755146079","https://openalex.org/W2794778778","https://openalex.org/W2808800115","https://openalex.org/W2903871660","https://openalex.org/W2908623803","https://openalex.org/W2963507686","https://openalex.org/W2965341826","https://openalex.org/W3080253043","https://openalex.org/W3179172661","https://openalex.org/W4291910343","https://openalex.org/W4312703862","https://openalex.org/W4383989010","https://openalex.org/W4388936714"],"related_works":[],"abstract_inverted_index":{"Spatial-temporal":[0],"prediction":[1,59],"has":[2,164],"become":[3],"an":[4,80],"important":[5],"task":[6],"in":[7,139],"many":[8],"applications,":[9],"such":[10],"as":[11],"traffic":[12,131,168],"forecasting.":[13],"Due":[14],"to":[15,30,94,151],"the":[16,32,41,46,67,75,91,102,117,126,144,153],"spatial-temporal":[17],"nature":[18],"of":[19,38,119],"data,":[20],"most":[21,37],"state-of-the-art":[22,167],"methods":[23],"heavily":[24],"depend":[25],"on":[26,63,171],"graph":[27,120],"neural":[28,92],"networks":[29],"model":[31],"inherent":[33],"spatial":[34,42,68,145],"relationships.":[35],"However,":[36],"them":[39],"process":[40],"data":[43],"by":[44],"applying":[45],"prior":[47],"adjacency":[48,55,82],"knowledge":[49,115],"or":[50],"learning":[51,79],"a":[52],"static":[53],"adaptive":[54,81],"matrix.":[56],"Thus,":[57],"their":[58],"performance":[60],"is":[61],"limited":[62],"dynamic":[64,113,127,135,148],"situations":[65],"where":[66],"dependencies":[69],"change":[70],"w.r.t":[71],"time.":[72],"Furthermore,":[73],"considering":[74],"stochastic":[76],"training":[77,118],"process,":[78],"matrix":[83],"from":[84],"scratch":[85],"also":[86],"makes":[87],"it":[88],"difficult":[89],"for":[90],"network":[93],"achieve":[95],"stable":[96],"parameters":[97],"and":[98],"performance.":[99],"To":[100],"address":[101],"above":[103],"challenges,":[104],"this":[105],"article":[106],"proposes":[107],"three":[108],"practical":[109],"extensions":[110],"that":[111,161],"incorporate":[112],"causal":[114,128,136,149],"into":[116],"convolution":[121],"networks.":[122],"We":[123],"first":[124],"analyze":[125],"graphs":[129,150],"between":[130],"nodes":[132],"with":[133],"one":[134],"discovery":[137],"algorithm":[138],"each":[140],"extended":[141],"model.":[142],"Subsequently,":[143],"module":[146],"employs":[147],"reveal":[152],"evolving":[154],"connections":[155],"among":[156],"nodes.":[157],"Extensive":[158],"experiments":[159],"demonstrate":[160],"our":[162],"method":[163],"successfully":[165],"enhanced":[166],"forecasting":[169],"models":[170],"two":[172],"benchmarks.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-11-23T00:00:00"}