{"id":"https://openalex.org/W4402352025","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650073","title":"Self-Supervised Masked Hypergraph Autoencoders for Spatio-Temporal Forecasting","display_name":"Self-Supervised Masked Hypergraph Autoencoders for Spatio-Temporal Forecasting","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352025","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650073"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5113373657","display_name":"Yuanpei Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanpei Huang","raw_affiliation_strings":["South China University of Techology,School of Computer Science &#x0026; Engineering,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Techology,School of Computer Science &#x0026; Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102943111","display_name":"Nanfeng Xiao","orcid":"https://orcid.org/0000-0002-2881-4239"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nanfeng Xiao","raw_affiliation_strings":["South China University of Techology,School of Computer Science &#x0026; Engineering,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Techology,School of Computer Science &#x0026; Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5113373657"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.2787,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56393787,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9879999756813049,"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"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9840999841690063,"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/hypergraph","display_name":"Hypergraph","score":0.8310551047325134},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7171651124954224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.520438015460968},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36177027225494385},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11723253130912781}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.8310551047325134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7171651124954224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.520438015460968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36177027225494385},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11723253130912781},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2078923569","https://openalex.org/W2080731889","https://openalex.org/W2194775991","https://openalex.org/W2592023122","https://openalex.org/W2756203131","https://openalex.org/W2892880750","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2916752133","https://openalex.org/W2917442156","https://openalex.org/W2964015378","https://openalex.org/W2965341826","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W3030163527","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3080997787","https://openalex.org/W3103720336","https://openalex.org/W3170140111","https://openalex.org/W3174022889","https://openalex.org/W3176075655","https://openalex.org/W3208592130","https://openalex.org/W4224911291","https://openalex.org/W4280490805","https://openalex.org/W4283315029","https://openalex.org/W4289533863","https://openalex.org/W4289533938","https://openalex.org/W4290876361","https://openalex.org/W4306317966","https://openalex.org/W4313156423","https://openalex.org/W4367047461","https://openalex.org/W4385270450","https://openalex.org/W4387846511","https://openalex.org/W6726873649","https://openalex.org/W6736685754","https://openalex.org/W6746015598","https://openalex.org/W6755207826","https://openalex.org/W6780221082","https://openalex.org/W6784998633","https://openalex.org/W6789531297","https://openalex.org/W6838789689","https://openalex.org/W6847964452","https://openalex.org/W6858721613"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4376608589","https://openalex.org/W1537073411","https://openalex.org/W3138003926","https://openalex.org/W1630514295","https://openalex.org/W4300037846","https://openalex.org/W4288275998","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Spatio-temporal":[0],"forecasting":[1,29,102,193],"has":[2,75],"become":[3,76],"a":[4,40,117,132,148,159],"critical":[5],"research":[6],"area":[7],"with":[8,60,89],"various":[9],"applications":[10,92],"in":[11,28,42,64,70,97,185],"modern":[12],"urban":[13],"environments.":[14],"Recent":[15],"works":[16],"have":[17],"employed":[18],"spatial-temporal":[19],"graph":[20,57,66,72,84,95,99],"neural":[21],"networks":[22],"to":[23,48,78,152,164],"model":[24],"complex":[25,105],"spatio-temporal":[26,37,80,101,170,192],"relationships":[27,38,52],"tasks.":[30],"However,":[31],"the":[32,61,90,187],"representation":[33],"capacity":[34],"of":[35,93,190],"intricate":[36],"remains":[39],"bottleneck":[41],"current":[43],"approaches.":[44],"They":[45],"both":[46,138],"fail":[47],"capture":[49,165],"non-pairwise":[50],"spatiotemporal":[51],"and":[53,106,140,146,168,181],"establish":[54],"real-time":[55,160,166],"dynamic":[56,161],"structures.":[58],"Subsequently,":[59],"great":[62],"advances":[63],"generative":[65,83],"self-supervised":[67,85],"learning,":[68],"particularly":[69],"masked":[71,94,133],"autoencoders,":[73],"it":[74],"possible":[77],"construct":[79],"representations":[81],"through":[82],"learning.":[86],"Nevertheless,":[87],"compared":[88],"general":[91],"autoencoders":[96],"other":[98],"tasks,":[100],"is":[103],"more":[104],"requires":[107],"further":[108],"architectural":[109],"design.":[110],"To":[111],"address":[112],"these":[113],"issues,":[114],"we":[115,157],"propose":[116],"novel":[118],"method":[119,130,176],"called":[120],"Self-Supervised":[121],"Masked":[122],"Hypergraph":[123],"Autoencoders":[124],"for":[125],"Spatio-Temporal":[126],"Forecasting":[127],"(MAE-ST).":[128],"Our":[129],"introduces":[131],"hypergraph":[134,144,162],"autoencoder":[135],"that":[136],"models":[137],"spatial":[139],"temporal":[141],"dependencies":[142],"as":[143],"structures":[145],"incorporates":[147],"special":[149],"remasking":[150],"strategy":[151],"regularize":[153],"feature":[154],"reconstruction.":[155],"Additionally,":[156],"incorporate":[158],"structure":[163],"dynamics":[167],"nonpairwise":[169],"dependencies.":[171],"We":[172],"evaluate":[173],"our":[174],"proposed":[175],"on":[177],"four":[178],"public":[179],"datasets":[180],"demonstrate":[182],"its":[183],"effectiveness":[184],"improving":[186],"prediction":[188],"performance":[189],"downstream":[191],"methods.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
