{"id":"https://openalex.org/W4409357082","doi":"https://doi.org/10.1109/access.2025.3555264","title":"A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks","display_name":"A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409357082","doi":"https://doi.org/10.1109/access.2025.3555264"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3555264","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3555264","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.2025.3555264","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104287435","display_name":"Zhixin Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhixin Xia","raw_affiliation_strings":["School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035644158","display_name":"Zhangqi Zheng","orcid":"https://orcid.org/0000-0001-9085-4277"},"institutions":[{"id":"https://openalex.org/I48381559","display_name":"Hebei Normal University of Science and Technology","ror":"https://ror.org/05g1mag11","country_code":"CN","type":"education","lineage":["https://openalex.org/I48381559"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhangqi Zheng","raw_affiliation_strings":["School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China","institution_ids":["https://openalex.org/I48381559"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001933478","display_name":"Fuyi Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiyang Wei","raw_affiliation_strings":["School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076346139","display_name":"Yongshan Liu","orcid":"https://orcid.org/0000-0002-6891-340X"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongshan Liu","raw_affiliation_strings":["School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103279050","display_name":"Lu Yu","orcid":"https://orcid.org/0000-0002-2938-9537"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu Yu","raw_affiliation_strings":["Hebei Port Group Datalink Technology (Xiongan) Company Ltd., Baoding, China"],"affiliations":[{"raw_affiliation_string":"Hebei Port Group Datalink Technology (Xiongan) Company Ltd., Baoding, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5104287435"],"corresponding_institution_ids":["https://openalex.org/I39333907"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06710385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"61675","last_page":"61684"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9872999787330627,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9872999787330627,"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.9067000150680542,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.7023347020149231},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.5571900606155396},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.518196165561676},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4952293932437897},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4715732932090759},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4428442716598511},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2992187738418579},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2855989933013916},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.22043582797050476},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1924436092376709}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7023347020149231},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.5571900606155396},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.518196165561676},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4952293932437897},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4715732932090759},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4428442716598511},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2992187738418579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2855989933013916},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.22043582797050476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1924436092376709},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3555264","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3555264","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:77a97bf17b4846f0873ba4f12ea62343","is_oa":true,"landing_page_url":"https://doaj.org/article/77a97bf17b4846f0873ba4f12ea62343","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 61675-61684 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3555264","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3555264","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":[],"awards":[{"id":"https://openalex.org/G3016906662","display_name":null,"funder_award_id":"61972334","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2808549270","https://openalex.org/W2907492528","https://openalex.org/W2908138230","https://openalex.org/W2963241486","https://openalex.org/W2998313947","https://openalex.org/W3004615113","https://openalex.org/W3091817824","https://openalex.org/W3125354632","https://openalex.org/W3137176540","https://openalex.org/W3204508881","https://openalex.org/W4205195364","https://openalex.org/W4285806099","https://openalex.org/W4285809121","https://openalex.org/W4324119760","https://openalex.org/W4366310338","https://openalex.org/W4385627173","https://openalex.org/W4387618988","https://openalex.org/W4388427854","https://openalex.org/W4388947331","https://openalex.org/W4389371798","https://openalex.org/W4391824604","https://openalex.org/W4392902760","https://openalex.org/W4392974974","https://openalex.org/W4393029488","https://openalex.org/W4401878952","https://openalex.org/W4406825430"],"related_works":["https://openalex.org/W1518185400","https://openalex.org/W3200586296","https://openalex.org/W4230332972","https://openalex.org/W1998033311","https://openalex.org/W4247322236","https://openalex.org/W1762272577","https://openalex.org/W4206960768","https://openalex.org/W4229899156","https://openalex.org/W2158247860","https://openalex.org/W4295104149"],"abstract_inverted_index":{"Port":[0,24],"network":[1,20,25,46,66,98,126,178,206],"information":[2,42],"security":[3,175],"has":[4],"received":[5],"extensive":[6],"attention":[7],"in":[8,11,18,220],"recent":[9],"years,":[10],"which":[12,68],"the":[13,19,41,44,49,55,81,84,88,91,96,102,108,118,124,138,144,154,174,193,197,201,211],"prediction":[14,50,56,198],"of":[15,43,83,176,200],"node":[16,99,120],"links":[17,100],"is":[21,26,208],"significant.":[22],"A":[23],"a":[27,61],"dynamic":[28,45,85,189],"network,":[29],"and":[30,47,76,106,116,142,161,192,218],"its":[31],"structure":[32],"changes":[33],"continuously":[34],"with":[35],"time.":[36],"Therefore,":[37],"to":[38,79,136,172],"effectively":[39],"utilize":[40],"improve":[48],"efficiency":[51],"as":[52,54,215],"well":[53],"accuracy,":[57],"this":[58,129,181],"paper":[59,130,182],"proposes":[60],"spatio-temporal":[62,202],"tensor":[63,203],"graph":[64,70,103,190,204],"neural":[65,205],"model,":[67],"learns":[69],"structural":[71],"features":[72,94,110,141,160],"from":[73],"both":[74],"spatial":[75,93,109,159],"temporal":[77,127,140,145,162],"aspects":[78],"capture":[80],"evolution":[82],"network.":[86],"Firstly,":[87],"model":[89,135,207],"extracts":[90],"corresponding":[92,139],"for":[95,123,169],"port":[97,125,177],"using":[101],"compression":[104],"technique":[105],"updates":[107],"based":[111,152],"on":[112,153,185],"GraphSAGE":[113],"by":[114,149],"sampling":[115],"aggregating":[117],"neighbor":[119],"features.":[121],"Secondly,":[122],"links,":[128],"introduces":[131],"an":[132],"exponential":[133],"decay":[134],"extract":[137],"obtains":[143],"edge":[146,163],"embedding":[147,164],"vectors":[148,165],"convolution":[150],"method":[151],"GCN":[155],"model.":[156],"The":[157],"updated":[158],"are":[166],"then":[167],"weighted":[168],"feature":[170],"fusion":[171],"predict":[173],"links.":[179],"Finally,":[180],"conducts":[183],"experiments":[184],"seven":[186],"publicly":[187],"available":[188],"datasets,":[191],"results":[194],"show":[195],"that":[196],"accuracy":[199],"better":[209],"than":[210],"baseline":[212],"models,":[213],"such":[214],"GC-LSTM,":[216],"HTGN,":[217],"GAT,":[219],"general.":[221]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}