{"id":"https://openalex.org/W3201545219","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534063","title":"MAGCN: A Multi-Adaptive Graph Convolutional Network for Traffic Forecasting","display_name":"MAGCN: A Multi-Adaptive Graph Convolutional Network for Traffic Forecasting","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201545219","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534063","mag":"3201545219"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5008947580","display_name":"Qingyuan Zhan","orcid":"https://orcid.org/0000-0003-0021-0270"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qingyuan Zhan","raw_affiliation_strings":["School of Software Engineering, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100890635","display_name":"Guixing Wu","orcid":"https://orcid.org/0000-0001-5743-1555"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guixing Wu","raw_affiliation_strings":["School of Software Engineering, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040877128","display_name":"Chuang Gan","orcid":"https://orcid.org/0000-0003-4031-5886"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Gan","raw_affiliation_strings":["School of Software Engineering, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008947580"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.3559,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.58802745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"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":1.0,"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":1.0,"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":0.9972000122070312,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9940999746322632,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7910593152046204},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6175734400749207},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.5506821870803833},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5195925235748291},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4445037841796875},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4318036437034607},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4196477234363556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3160546123981476},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.11103901267051697}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7910593152046204},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6175734400749207},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.5506821870803833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5195925235748291},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4445037841796875},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4318036437034607},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4196477234363556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3160546123981476},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11103901267051697},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W20141250","https://openalex.org/W43001522","https://openalex.org/W637153065","https://openalex.org/W1522301498","https://openalex.org/W1973943669","https://openalex.org/W1983883318","https://openalex.org/W1984969638","https://openalex.org/W2036785686","https://openalex.org/W2530386080","https://openalex.org/W2582812851","https://openalex.org/W2756203131","https://openalex.org/W2884585870","https://openalex.org/W2892288283","https://openalex.org/W2899771611","https://openalex.org/W2903871660","https://openalex.org/W2948058585","https://openalex.org/W2962767366","https://openalex.org/W2963076818","https://openalex.org/W2963358464","https://openalex.org/W2963403868","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2964113829","https://openalex.org/W2964121744","https://openalex.org/W2964145825","https://openalex.org/W2964319113","https://openalex.org/W2964321699","https://openalex.org/W2965341826","https://openalex.org/W2965806703","https://openalex.org/W2996847713","https://openalex.org/W3015712039","https://openalex.org/W3103720336","https://openalex.org/W4230512065","https://openalex.org/W4285723986","https://openalex.org/W4294558607","https://openalex.org/W4385245566","https://openalex.org/W6600828923","https://openalex.org/W6620673361","https://openalex.org/W6631190155","https://openalex.org/W6685350579","https://openalex.org/W6713582119","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6739901393","https://openalex.org/W6746015598","https://openalex.org/W6753412334","https://openalex.org/W6754410629","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2045049461","https://openalex.org/W1978893398","https://openalex.org/W2201908702","https://openalex.org/W4381094582","https://openalex.org/W2369625323","https://openalex.org/W2364579609","https://openalex.org/W1977906818","https://openalex.org/W1522139108","https://openalex.org/W2353528968","https://openalex.org/W2032776242"],"abstract_inverted_index":{"Traffic":[0],"forecasting":[1,55,84],"is":[2,45,154],"one":[3],"of":[4,33,53,95,111,119,150,176,190],"the":[5,48,59,65,93,96,109,117,129,144,161,174,188,194],"most":[6],"fundamental":[7],"components":[8],"in":[9,85,101,128,156],"many":[10],"applications":[11],"from":[12],"urban":[13],"computing":[14],"to":[15,121,123,142,158,172],"intelligent":[16],"transportation.":[17],"Recently,":[18],"graph":[19,42,97,112],"convolutional":[20],"networks":[21],"(GCNs),":[22],"which":[23,44,153],"model":[24,89,120],"traffic":[25,54,83,151],"data":[26,125],"as":[27],"spatiotemporal":[28],"graph,":[29],"have":[30],"attracted":[31],"lots":[32],"attention.":[34],"However,":[35],"existing":[36],"GCNs":[37,100],"mainly":[38],"focus":[39],"on":[40,182],"pre-defined":[41],"structure":[43],"fixed":[46],"over":[47],"entire":[49],"network.":[50],"These":[51],"methods":[52],"can":[56,90],"not":[57],"capture":[58,143,160],"complex":[60,177],"spatial":[61,178],"correlations":[62,149],"especially":[63],"for":[64,82],"higher":[66],"level":[67],"features.":[68],"To":[69],"address":[70],"these":[71],"problems,":[72],"we":[73,132,166],"propose":[74],"a":[75,134,168],"novel":[76,135],"Multi-Adaptive":[77],"Graph":[78,138],"Convolutional":[79,139],"Network":[80,140],"(MAGCN)":[81],"this":[86],"work.":[87],"Our":[88],"dynamically":[91],"learn":[92],"topology":[94],"through":[98],"multi-range":[99],"an":[102],"end-to-end":[103],"manner.":[104],"This":[105],"data-driven":[106],"method":[107],"makes":[108],"construction":[110],"more":[113],"flexible":[114],"and":[115,146],"increases":[116],"generality":[118],"adapt":[122],"various":[124],"samples.":[126],"Moreover,":[127],"proposed":[130],"framework,":[131],"design":[133],"Differential":[136],"Temporal":[137],"(DTGCN)":[141],"periodic":[145],"immediate":[147],"temporal":[148],"data,":[152],"integrated":[155],"MAGCN":[157,192],"effectively":[159],"dynamic":[162],"spatial-temporal":[163],"dependencies.":[164],"Besides,":[165],"adopt":[167],"multi-subgraph":[169],"encoding":[170],"mechanism":[171],"enhance":[173],"representation":[175],"dependency.":[179],"Extensive":[180],"experiments":[181],"two":[183],"real-world":[184],"datasets":[185],"demonstrate":[186],"that":[187],"performance":[189],"our":[191],"exceeds":[193],"state-of-the-art":[195],"baselines.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}