{"id":"https://openalex.org/W3115700835","doi":"https://doi.org/10.1145/3437963.3441731","title":"Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction","display_name":"Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction","publication_year":2021,"publication_date":"2021-03-06","ids":{"openalex":"https://openalex.org/W3115700835","doi":"https://doi.org/10.1145/3437963.3441731","mag":"3115700835"},"language":"en","primary_location":{"id":"doi:10.1145/3437963.3441731","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441731","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","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/A5100378255","display_name":"Chunyang Wang","orcid":"https://orcid.org/0000-0002-1752-5423"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunyang Wang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081759167","display_name":"Yanmin Zhu","orcid":"https://orcid.org/0000-0001-6406-4992"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanmin Zhu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082218936","display_name":"Tianzi Zang","orcid":"https://orcid.org/0000-0001-9390-3740"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianzi Zang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041155283","display_name":"Haobing Liu","orcid":"https://orcid.org/0000-0002-2546-3306"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haobing Liu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012589427","display_name":"Jiadi Yu","orcid":"https://orcid.org/0000-0002-0207-9643"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiadi Yu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100378255"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":5.2781,"has_fulltext":false,"cited_by_count":80,"citation_normalized_percentile":{"value":0.96797244,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"616","last_page":"634"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7688888311386108},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.6090121865272522},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5691846609115601},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5570183396339417},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.478397011756897},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.455900639295578},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4392136037349701},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.4335448741912842},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4323320686817169},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4248884916305542},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.41206085681915283},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4108920097351074},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19581729173660278},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1485864222049713},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11862584948539734},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09453460574150085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7688888311386108},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.6090121865272522},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5691846609115601},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5570183396339417},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.478397011756897},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.455900639295578},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4392136037349701},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.4335448741912842},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4323320686817169},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4248884916305542},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.41206085681915283},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4108920097351074},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19581729173660278},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1485864222049713},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11862584948539734},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09453460574150085},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3437963.3441731","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441731","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"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":26,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W1969865391","https://openalex.org/W1971402834","https://openalex.org/W2146848957","https://openalex.org/W2157539394","https://openalex.org/W2315455726","https://openalex.org/W2525579820","https://openalex.org/W2530443992","https://openalex.org/W2756203131","https://openalex.org/W2767894694","https://openalex.org/W2782920454","https://openalex.org/W2808535700","https://openalex.org/W2809035759","https://openalex.org/W2903871660","https://openalex.org/W2904832339","https://openalex.org/W2949888546","https://openalex.org/W2950817888","https://openalex.org/W2952135817","https://openalex.org/W2963984147","https://openalex.org/W2964927812","https://openalex.org/W2965341826","https://openalex.org/W2965399951","https://openalex.org/W2965806703","https://openalex.org/W3002343708","https://openalex.org/W3103720336","https://openalex.org/W3123995874"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2103419012","https://openalex.org/W2988126442","https://openalex.org/W1974414866","https://openalex.org/W2057568687","https://openalex.org/W2611163850","https://openalex.org/W2063982682","https://openalex.org/W3005768482"],"abstract_inverted_index":{"Air":[0],"pollution":[1],"is":[2,18],"an":[3,86,158],"important":[4],"environmental":[5],"issue":[6],"of":[7,29,39,102,111,191],"increasing":[8],"concern,":[9],"which":[10,68,132],"impacts":[11],"human":[12],"health.":[13],"Accurate":[14],"air":[15,26,40,99],"quality":[16,41,100],"prediction":[17,101],"crucial":[19],"for":[20,42,98,178],"avoiding":[21],"people":[22],"suffering":[23],"from":[24,149],"serious":[25],"pollution.":[27],"Most":[28],"the":[30,36,66,140,169,188],"prior":[31],"works":[32],"focus":[33],"on":[34,183],"capturing":[35],"temporal":[37,74,121],"trend":[38],"each":[43],"monitoring":[44],"station.":[45],"Recent":[46],"deep":[47],"learning":[48],"based":[49],"methods":[50],"also":[51,77],"model":[52,94,193],"spatial":[53,116],"dependencies":[54],"among":[55,113],"neighboring":[56],"stations.":[57,104,180],"However,":[58],"we":[59,84,106,127],"observe":[60],"that":[61],"besides":[62],"geospatially":[63],"adjacent":[64],"stations,":[65],"stations":[67,114,151],"share":[69],"similar":[70,166],"functionalities":[71],"or":[72],"consistent":[73],"patterns":[75],"could":[76],"have":[78],"strong":[79],"dependencies.":[80],"In":[81],"this":[82],"paper,":[83],"propose":[85],"Attentive":[87],"Temporal":[88],"Graph":[89],"Convolutional":[90],"Network":[91],"(ATGCN)":[92],"to":[93,145,168,174],"diverse":[95],"inter-station":[96],"relationships":[97,112],"citywide":[103],"Specifically,":[105],"first":[107],"encode":[108],"three":[109],"types":[110],"including":[115],"adjacency,":[117],"functional":[118],"similarity,":[119],"and":[120],"pattern":[122],"similarity":[123],"into":[124,139],"graphs.":[125,154],"Then":[126],"design":[128],"parallel":[129],"encoding":[130,170],"modules,":[131],"respectively":[133],"incorporate":[134],"attentive":[135],"graph":[136],"convolution":[137],"operations":[138],"Gated":[141],"Recurrent":[142],"Units":[143],"(GRUs)":[144],"iteratively":[146],"aggregate":[147],"features":[148],"related":[150],"with":[152,157,164],"different":[153],"Furthermore,":[155],"augmented":[156],"attention-based":[159],"fusion":[160],"unit,":[161],"decoding":[162],"modules":[163,171],"a":[165],"structure":[167],"are":[172],"designed":[173],"generate":[175],"multi-step":[176],"predictions":[177],"all":[179],"The":[181],"experiments":[182],"two":[184],"real-world":[185],"datasets":[186],"demonstrate":[187],"superior":[189],"performance":[190],"our":[192],"beyond":[194],"state-of-the-art":[195],"methods.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
