{"id":"https://openalex.org/W3080403679","doi":"https://doi.org/10.1145/3394486.3403273","title":"Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data","display_name":"Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3080403679","doi":"https://doi.org/10.1145/3394486.3403273","mag":"3080403679"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5062365263","display_name":"Fengli Xu","orcid":"https://orcid.org/0000-0002-5720-4026"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengli Xu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355277","display_name":"Yong Li","orcid":"https://orcid.org/0000-0001-5617-1659"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014274752","display_name":"Shusheng Xu","orcid":"https://orcid.org/0000-0002-6016-6112"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shusheng Xu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":5.6539,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.96894689,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2225","last_page":"2233"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9995999932289124,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9871000051498413,"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/T11645","display_name":"Urban, Neighborhood, and Segregation Studies","score":0.9837999939918518,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/news-aggregator","display_name":"News aggregator","score":0.7699472904205322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7503907680511475},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5893688201904297},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.4713432490825653},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38708990812301636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3562178611755371},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2823554277420044},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.1083114743232727}],"concepts":[{"id":"https://openalex.org/C180505990","wikidata":"https://www.wikidata.org/wiki/Q498267","display_name":"News aggregator","level":2,"score":0.7699472904205322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7503907680511475},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5893688201904297},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.4713432490825653},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38708990812301636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3562178611755371},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2823554277420044},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1083114743232727},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394486.3403273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W1572874895","https://openalex.org/W1888005072","https://openalex.org/W1977177161","https://openalex.org/W2000603369","https://openalex.org/W2066806488","https://openalex.org/W2069040504","https://openalex.org/W2117821362","https://openalex.org/W2137226992","https://openalex.org/W2139380543","https://openalex.org/W2151405152","https://openalex.org/W2154077228","https://openalex.org/W2154851992","https://openalex.org/W2270761083","https://openalex.org/W2318951588","https://openalex.org/W2471528185","https://openalex.org/W2514936170","https://openalex.org/W2520457855","https://openalex.org/W2520775317","https://openalex.org/W2596585349","https://openalex.org/W2612690371","https://openalex.org/W2620731571","https://openalex.org/W2624431344","https://openalex.org/W2797803538","https://openalex.org/W2807021761","https://openalex.org/W2809366716","https://openalex.org/W2809583854","https://openalex.org/W2889833307","https://openalex.org/W2898986204","https://openalex.org/W2911286998","https://openalex.org/W2914657261","https://openalex.org/W2962756421","https://openalex.org/W2963403868","https://openalex.org/W2964165509","https://openalex.org/W3023146776","https://openalex.org/W3037085755","https://openalex.org/W3100848837","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W3121660921","https://openalex.org/W3125710686","https://openalex.org/W3150918741","https://openalex.org/W4236768082","https://openalex.org/W6634191298","https://openalex.org/W6679935232","https://openalex.org/W6680532697","https://openalex.org/W6682508806","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W3036238356","https://openalex.org/W2767445978","https://openalex.org/W2603387358","https://openalex.org/W3092831610","https://openalex.org/W230187509","https://openalex.org/W4206057490","https://openalex.org/W962287279","https://openalex.org/W3112734853","https://openalex.org/W2012785328","https://openalex.org/W2342558870"],"abstract_inverted_index":{"We":[0],"study":[1],"the":[2,25,41,53,72,82,88,101,121,138,146,154],"problem":[3,104],"of":[4,8,50,60,136],"predicting":[5],"regional":[6,171],"economy":[7,172],"U.S.":[9,17],"counties":[10],"with":[11,47,91,105],"open":[12],"migration":[13,42,54,73,174],"data":[14,102],"collected":[15,56],"from":[16,57,71],"Internal":[18],"Revenue":[19],"Service":[20],"(IRS)":[21],"records.":[22],"To":[23],"capture":[24],"complicated":[26],"correlations":[27,169],"between":[28,170],"them,":[29],"we":[30,99],"design":[31],"a":[32,45,92],"novel":[33],"Attentional":[34],"Multi-graph":[35],"Convolutional":[36],"Network":[37],"(AMCN),":[38],"which":[39],"models":[40],"behavior":[43],"as":[44],"multi-graph":[46,74],"different":[48,61,64],"types":[49],"edges":[51],"denoting":[52],"flows":[55],"heterogeneous":[58],"sources":[59],"years":[62],"and":[63,85,150,173],"demographics.":[65],"AMCN":[66,128,165],"extracts":[67],"high":[68],"quality":[69],"feature":[70],"by":[75,143,151],"first":[76],"applying":[77],"customized":[78],"aggregator":[79,95],"functions":[80],"on":[81],"induced":[83],"subgraphs,":[84],"then":[86],"fusing":[87],"aggregated":[89],"features":[90],"higher-order":[93],"attentional":[94],"function.":[96],"In":[97],"addition,":[98],"address":[100],"sparsity":[103],"an":[106],"important":[107,115],"neighbor":[108],"discovery":[109],"algorithm":[110],"that":[111,117],"can":[112],"automatically":[113],"supplement":[114],"neighbors":[116],"are":[118],"absent":[119],"in":[120,134],"empirical":[122],"data.":[123,175],"Experiment":[124],"results":[125],"show":[126],"our":[127,163],"model":[129,149,160,166],"significantly":[130],"outperforms":[131],"all":[132],"baselines":[133],"terms":[135],"reducing":[137],"relative":[139],"mean":[140],"square":[141],"error":[142],"43.8%":[144],"against":[145,153],"classic":[147],"regression":[148],"12.7%":[152],"state-of-the-art":[155],"deep":[156],"learning":[157],"baselines.":[158],"In-depth":[159],"analysis":[161],"shows":[162],"proposed":[164],"reveals":[167],"insightful":[168]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
