{"id":"https://openalex.org/W4320024269","doi":"https://doi.org/10.1109/bigdata55660.2022.10021066","title":"Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder","display_name":"Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024269","doi":"https://doi.org/10.1109/bigdata55660.2022.10021066"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10021066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10021066","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2408.01938","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060950357","display_name":"Naoto Minakawa","orcid":"https://orcid.org/0000-0003-0869-2657"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Naoto Minakawa","raw_affiliation_strings":["The University of Tokyo,School of Engineering,Department of Systems Innovation,Tokyo,Japan","Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,School of Engineering,Department of Systems Innovation,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044205949","display_name":"Kiyoshi Izumi","orcid":"https://orcid.org/0000-0003-0870-7310"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kiyoshi Izumi","raw_affiliation_strings":["The University of Tokyo,School of Engineering,Department of Systems Innovation,Tokyo,Japan","Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,School of Engineering,Department of Systems Innovation,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028823648","display_name":"Hiroki Sakaji","orcid":"https://orcid.org/0000-0001-5030-625X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Sakaji","raw_affiliation_strings":["The University of Tokyo,School of Engineering,Department of Systems Innovation,Tokyo,Japan","Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,School of Engineering,Department of Systems Innovation,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060950357"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.5218,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.65559089,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2327","last_page":"2332"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9857000112533569,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9621000289916992,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gravity-model-of-trade","display_name":"Gravity model of trade","score":0.8618727922439575},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.577734112739563},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5255081653594971},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.49336376786231995},{"id":"https://openalex.org/keywords/government","display_name":"Government (linguistics)","score":0.4214757978916168},{"id":"https://openalex.org/keywords/international-trade","display_name":"International trade","score":0.2975821793079376},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.22638368606567383},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.22083190083503723}],"concepts":[{"id":"https://openalex.org/C87889798","wikidata":"https://www.wikidata.org/wiki/Q1543986","display_name":"Gravity model of trade","level":2,"score":0.8618727922439575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.577734112739563},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5255081653594971},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.49336376786231995},{"id":"https://openalex.org/C2778137410","wikidata":"https://www.wikidata.org/wiki/Q2732820","display_name":"Government (linguistics)","level":2,"score":0.4214757978916168},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.2975821793079376},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22638368606567383},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.22083190083503723},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10021066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10021066","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2408.01938","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.01938","pdf_url":"https://arxiv.org/pdf/2408.01938","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2408.01938","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.01938","pdf_url":"https://arxiv.org/pdf/2408.01938","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G8754627776","display_name":null,"funder_award_id":"JPMJMI20B1","funder_id":"https://openalex.org/F4320338243","funder_display_name":"JST-Mirai Program"}],"funders":[{"id":"https://openalex.org/F4320338243","display_name":"JST-Mirai Program","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320024269.pdf","grobid_xml":"https://content.openalex.org/works/W4320024269.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2059580880","https://openalex.org/W2125084340","https://openalex.org/W2135296617","https://openalex.org/W2787097171","https://openalex.org/W3125442684","https://openalex.org/W3133596390","https://openalex.org/W3141702300","https://openalex.org/W3157286395","https://openalex.org/W3177445356","https://openalex.org/W4206482253","https://openalex.org/W4213183334","https://openalex.org/W4225709198","https://openalex.org/W4281747823","https://openalex.org/W4293261592","https://openalex.org/W6631699956","https://openalex.org/W6678423695","https://openalex.org/W6726873649","https://openalex.org/W6730084236","https://openalex.org/W6794315554","https://openalex.org/W6795768889"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0],"gravity":[1,58,95,134,142,181],"models":[2,59,96],"has":[3],"been":[4],"studied":[5],"to":[6,44,71,79,101,167,178],"analyze":[7],"interaction":[8,63,70],"between":[9,16,23,31,64,88],"two":[10,32],"objects":[11],"such":[12,62],"as":[13,147],"trade":[14,40,115,137,174],"amount":[15,41,116,138,175],"a":[17,24],"pair":[18,25],"of":[19,26],"countries,":[20],"human":[21],"migration":[22],"countries":[27],"and":[28,46,52,93,125,155,159],"traffic":[29],"flow":[30],"cities.":[33],"Particularly":[34],"in":[35,48,97,109,153],"the":[36,57,66,69,81,86,94,110,133,141,179],"international":[37,98],"trade,":[38],"predicting":[39],"is":[42,130],"instrumental":[43],"industry":[45],"government":[47],"business":[49],"decision":[50],"making":[51],"determining":[53],"economic":[54],"policies.":[55],"Whereas":[56],"well":[60],"captures":[61],"objects,":[65],"model":[67,143,182],"simplifies":[68],"extract":[72],"essential":[73],"relationships":[74],"or":[75],"needs":[76],"handcrafted":[77],"features":[78],"drive":[80],"models.":[82],"Recent":[83],"studies":[84,108],"indicate":[85,168],"connection":[87],"graph":[89],"neural":[90],"networks":[91],"(GNNs)":[92],"trade.":[99],"However,":[100],"our":[102],"best":[103],"knowledge,":[104],"hardly":[105],"any":[106],"previous":[107],"this":[111],"domain":[112],"directly":[113],"predicts":[114],"by":[117,132,140,157,183],"GNNs.":[118],"We":[119],"propose":[120],"GGAE":[121,158,169],"(Gravity-informed":[122],"Graph":[123],"Auto-encoder)":[124],"its":[126,160],"surrogate":[127,161],"model,":[128,135],"which":[129],"inspired":[131],"showing":[136],"prediction":[139,151,176],"can":[144,172],"be":[145],"formulated":[146],"an":[148],"edge":[149],"weight":[150],"problem":[152],"GNNs":[154,171],"solved":[156],"model.":[162],"Furthermore,":[163],"we":[164],"conducted":[165],"experiments":[166],"with":[170],"improve":[173],"compared":[177],"traditional":[180],"considering":[184],"complex":[185],"relationships.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}