{"id":"https://openalex.org/W7123357992","doi":"https://doi.org/10.1109/tsmc.2025.3647753","title":"Causal Federated Graph Neural Networks for Multiobjective Facility Location","display_name":"Causal Federated Graph Neural Networks for Multiobjective Facility Location","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7123357992","doi":"https://doi.org/10.1109/tsmc.2025.3647753"},"language":null,"primary_location":{"id":"doi:10.1109/tsmc.2025.3647753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2025.3647753","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-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/A5122898020","display_name":"Xueming Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xueming Yan","raw_affiliation_strings":["School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7809-3436","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121167814","display_name":"Yaochu Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaochu Jin","raw_affiliation_strings":["School of Engineering, Westlake University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1100-0631","affiliations":[{"raw_affiliation_string":"School of Engineering, Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015163032","display_name":"Chuyue Wang","orcid":"https://orcid.org/0009-0009-9874-9880"},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuyue Wang","raw_affiliation_strings":["School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0009-9874-9880","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122647966","display_name":"Shangshang Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangshang Yang","raw_affiliation_strings":["School of Artificial Intelligence, Anhui University, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0003-0837-5424","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5122898020"],"corresponding_institution_ids":["https://openalex.org/I186272606"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0670581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"56","issue":"3","first_page":"1832","last_page":"1845"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11502","display_name":"Facility Location and Emergency Management","score":0.40630000829696655,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11502","display_name":"Facility Location and Emergency Management","score":0.40630000829696655,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.07940000295639038,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.0729999989271164,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.5842999815940857},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5216000080108643},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.5072000026702881},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5037000179290771},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.41019999980926514},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.37049999833106995},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.35010001063346863},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.32839998602867126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7418000102043152},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.5842999815940857},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5216000080108643},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.5072000026702881},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5037000179290771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48730000853538513},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.46560001373291016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4187999963760376},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.358599990606308},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3082999885082245},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.2644999921321869},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsmc.2025.3647753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2025.3647753","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1820859487","display_name":null,"funder_award_id":"62576116","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6511873551","display_name":null,"funder_award_id":"62136003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multiobjective":[0],"facility":[1,77],"location":[2],"problems":[3],"(MO-FLPs)":[4],"are":[5],"common":[6],"in":[7,20,26,63],"real-world":[8],"applications,":[9],"involving":[10],"tradeoffs":[11],"among":[12],"cost,":[13],"reliability,":[14],"and":[15,79,94,154],"service":[16],"quality.":[17],"Recent":[18],"advances":[19],"deep":[21],"learning":[22,116],"have":[23],"shown":[24],"potential":[25],"solving":[27,61],"MO-FLPs;":[28],"however,":[29],"existing":[30],"approaches":[31],"often":[32],"require":[33],"centralized":[34],"data,":[35],"which":[36],"is":[37],"impractical":[38],"due":[39],"to":[40,73,158],"privacy":[41],"constraints":[42],"across":[43],"distributed":[44],"data":[45,119],"owners.":[46],"To":[47],"address":[48],"this":[49],"issue,":[50],"we":[51],"propose":[52],"a":[53,64,97,109,123,139],"causally":[54],"federated":[55,110],"graph":[56,86,100],"neural":[57,87],"network":[58],"(CFGNN)":[59],"for":[60],"MO-FLPs":[62,69],"privacy-preserving":[65],"manner.":[66],"We":[67],"represent":[68],"as":[70],"bipartite":[71],"graphs":[72],"capture":[74],"relationships":[75],"between":[76],"sites":[78],"customer":[80],"zones.":[81],"On":[82,105],"each":[83],"client,":[84],"dual":[85],"networks":[88],"(GNNs)":[89],"learn":[90],"representations":[91],"of":[92],"nodes":[93],"edges,":[95],"while":[96],"causal":[98,111,130],"instance":[99],"extracts":[101],"stable":[102],"interinstance":[103],"relationships.":[104],"the":[106],"server":[107],"side,":[108],"hypergraph":[112],"module":[113],"facilitates":[114],"collaborative":[115],"without":[117],"compromising":[118],"privacy.":[120],"In":[121],"addition,":[122],"multilayer":[124],"perceptron":[125],"(MLP)":[126],"surrogate":[127],"model":[128],"with":[129,144],"embeddings":[131],"generates":[132],"approximate":[133],"Pareto-optimal":[134],"solutions.":[135],"Extensive":[136],"experiments":[137],"on":[138],"newly":[140],"constructed":[141],"benchmark":[142],"dataset":[143],"different":[145],"scales":[146],"demonstrate":[147],"that":[148],"CFGNN":[149],"achieves":[150],"superior":[151],"solution":[152],"quality":[153],"generalization":[155],"performance":[156],"compared":[157],"state-of-the-art":[159],"approaches.":[160]},"counts_by_year":[],"updated_date":"2026-02-24T06:16:03.338239","created_date":"2026-01-14T00:00:00"}
