{"id":"https://openalex.org/W7105847959","doi":"https://doi.org/10.1109/access.2025.3633711","title":"SEAGNN: A Multi-Source Heterogeneous Road Network Matching Method With Enhanced Structural Awareness","display_name":"SEAGNN: A Multi-Source Heterogeneous Road Network Matching Method With Enhanced Structural Awareness","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7105847959","doi":"https://doi.org/10.1109/access.2025.3633711"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3633711","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3633711","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3633711","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Guangkun Ma","orcid":"https://orcid.org/0009-0000-0024-0803"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangkun Ma","raw_affiliation_strings":["School of Software, Shenyang University of Technology, Shenyang, China"],"raw_orcid":"https://orcid.org/0009-0000-0024-0803","affiliations":[{"raw_affiliation_string":"School of Software, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiazhuang Wang","orcid":"https://orcid.org/0009-0004-4575-0259"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiazhuang Wang","raw_affiliation_strings":["School of Software, Shenyang University of Technology, Shenyang, China"],"raw_orcid":"https://orcid.org/0009-0004-4575-0259","affiliations":[{"raw_affiliation_string":"School of Software, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhengang Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120147","display_name":"Shenyang Research Institute of Foundry","ror":"https://ror.org/02dzk0c85","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210120147"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengang Ren","raw_affiliation_strings":["Shenyang MXNavi Company Ltd., Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang MXNavi Company Ltd., Shenyang, China","institution_ids":["https://openalex.org/I4210120147"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kun Hao","orcid":"https://orcid.org/0000-0002-6834-9925"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Hao","raw_affiliation_strings":["School of Software, Shenyang University of Technology, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-6834-9925","affiliations":[{"raw_affiliation_string":"School of Software, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.7218,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.76384938,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"13","issue":null,"first_page":"197277","last_page":"197288"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.8898000121116638,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.8898000121116638,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.02810000069439411,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.01360000018030405,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6333000063896179},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.609000027179718},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.49549999833106995},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4478999972343445},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4332999885082245},{"id":"https://openalex.org/keywords/map-matching","display_name":"Map matching","score":0.3873000144958496},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3756999969482422},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.3569999933242798},{"id":"https://openalex.org/keywords/flow-network","display_name":"Flow network","score":0.35519999265670776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.782800018787384},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6333000063896179},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5712000131607056},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4478999972343445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41519999504089355},{"id":"https://openalex.org/C2778559875","wikidata":"https://www.wikidata.org/wiki/Q1892023","display_name":"Map matching","level":3,"score":0.3873000144958496},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C61455927","wikidata":"https://www.wikidata.org/wiki/Q1030529","display_name":"Blossom algorithm","level":3,"score":0.33719998598098755},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28839999437332153},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C49777392","wikidata":"https://www.wikidata.org/wiki/Q5535495","display_name":"Geometric networks","level":3,"score":0.25369998812675476},{"id":"https://openalex.org/C188048851","wikidata":"https://www.wikidata.org/wiki/Q2298569","display_name":"Road map","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3633711","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3633711","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f64027b7e940440ba8cd2a5995880412","is_oa":true,"landing_page_url":"https://doaj.org/article/f64027b7e940440ba8cd2a5995880412","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 197277-197288 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3633711","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3633711","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6495311260223389},{"id":"https://metadata.un.org/sdg/14","display_name":"Life below water","score":0.42178332805633545}],"awards":[{"id":"https://openalex.org/G2976991410","display_name":null,"funder_award_id":"2024-BSLH-165","funder_id":"https://openalex.org/F4320323190","funder_display_name":"Fujian Provincial Department of Science and Technology"},{"id":"https://openalex.org/G6399071294","display_name":null,"funder_award_id":"62402326","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"},{"id":"https://openalex.org/F4320323190","display_name":"Fujian Provincial Department of Science and Technology","ror":"https://ror.org/00rgzpv08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Road-network":[0],"matching":[1,56,130,134],"aims":[2],"to":[3,23,38,60,92,118],"align":[4],"corresponding":[5],"road":[6,52,106,128,148,183],"segments":[7],"and":[8,18,33,47,100,110,131,141,154,169,178],"their":[9],"topological":[10,45],"adjacencies":[11],"across":[12],"multiple":[13],"sources,":[14],"reconcile":[15],"geometric":[16,43],"discrepancies,":[17],"produce":[19],"a":[20,72,83,161,165],"unified":[21],"network":[22,53,129,149],"support":[24],"applications":[25],"such":[26,104],"as":[27,105],"navigation":[28],"data":[29],"fusion,":[30],"traffic":[31],"analysis,":[32],"emergency":[34],"route":[35],"planning.":[36],"Due":[37],"the":[39,89,97,113,120],"significant":[40],"heterogeneity":[41],"in":[42,127,136,181],"accuracy,":[44],"structures,":[46],"attribute":[48],"representations":[49],"among":[50],"multi-source":[51],"data,":[54],"existing":[55],"methods":[57],"often":[58],"fail":[59],"accurately":[61],"capture":[62],"complex":[63,182],"correspondences.":[64],"To":[65],"address":[66],"this":[67,69],"challenge,":[68],"paper":[70],"propose":[71],"Structure-Enhanced":[73],"Anchor":[74],"Graph":[75],"Neural":[76],"Network":[77],"(SEAGNN).":[78],"The":[79],"proposed":[80],"method":[81],"integrates":[82],"multi-feature":[84],"cross-graph":[85],"attention":[86],"module":[87],"into":[88],"SeedGNN":[90],"framework":[91],"enhance":[93],"structural":[94],"awareness,":[95],"enabling":[96],"adaptive":[98],"weighting":[99],"fusion":[101],"of":[102,163,167,172],"features":[103],"length,":[107],"orientation,":[108],"curvature,":[109],"topology.":[111],"Furthermore,":[112],"Sinkhorn":[114],"algorithm":[115],"is":[116],"adopted":[117],"replace":[119],"Hungarian":[121],"algorithm,":[122],"allowing":[123],"for":[124],"soft":[125],"assignment":[126],"thereby":[132],"mitigating":[133],"errors":[135],"scenarios":[137],"involving":[138],"many-to-many":[139],"correspondences":[140],"inconsistent":[142],"hop":[143],"counts.":[144],"Experiments":[145],"conducted":[146],"on":[147],"datasets":[150],"from":[151],"Boston,":[152],"USA,":[153],"Tokyo,":[155],"Japan,":[156],"show":[157],"that":[158],"SEAGNN":[159],"achieves":[160],"precision":[162],"87.57%,":[164],"recall":[166],"89.76%,":[168],"an":[170],"F1-score":[171],"88.65%,":[173],"demonstrating":[174],"its":[175],"superior":[176],"performance":[177],"generalization":[179],"capability":[180],"environments.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-25T14:43:58.451035","created_date":"2025-11-17T00:00:00"}
