{"id":"https://openalex.org/W4416251320","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227850","title":"Cross-Network Relationship Learning via Optimal Transport for Link Prediction","display_name":"Cross-Network Relationship Learning via Optimal Transport for Link Prediction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251320","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227850"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227850","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5091926843","display_name":"Zhifeng Hao","orcid":"https://orcid.org/0000-0002-9713-7251"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhifeng Hao","raw_affiliation_strings":["Guangdong University of Technology,School of Computer Science,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,School of Computer Science,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084139963","display_name":"Zihan Chen","orcid":"https://orcid.org/0000-0001-7799-9658"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihan Chen","raw_affiliation_strings":["Guangdong University of Technology,School of Computer Science,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,School of Computer Science,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442732","display_name":"Zhiwei Chen","orcid":"https://orcid.org/0009-0001-2403-3195"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwei Chen","raw_affiliation_strings":["Guangdong University of Technology,School of Computer Science,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,School of Computer Science,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060956246","display_name":"Yuguang Yan","orcid":"https://orcid.org/0000-0001-9879-4758"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuguang Yan","raw_affiliation_strings":["Guangdong University of Technology,School of Computer Science,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,School of Computer Science,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076948208","display_name":"Ruichu Cai","orcid":"https://orcid.org/0000-0001-8972-167X"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruichu Cai","raw_affiliation_strings":["Guangdong University of Technology,School of Computer Science,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,School of Computer Science,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5091926843"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18272544,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9879000186920166,"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":0.9879000186920166,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.006200000178068876,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.0010999999940395355,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6101999878883362},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5961999893188477},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.5579000115394592},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5503000020980835},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5038999915122986},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.49320000410079956},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42989999055862427},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.41269999742507935},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.40849998593330383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6833000183105469},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6101999878883362},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5961999893188477},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.5579000115394592},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5503000020980835},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.54830002784729},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5038999915122986},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.49320000410079956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4489000141620636},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.3978999853134155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3644999861717224},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3264999985694885},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C128356825","wikidata":"https://www.wikidata.org/wiki/Q738422","display_name":"Barycentric coordinate system","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C1173588","wikidata":"https://www.wikidata.org/wiki/Q6554294","display_name":"Link analysis","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C142323842","wikidata":"https://www.wikidata.org/wiki/Q7979900","display_name":"Weighted network","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227850","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1594039573","https://openalex.org/W2027842533","https://openalex.org/W2030252673","https://openalex.org/W2036996178","https://openalex.org/W2134670261","https://openalex.org/W2143668817","https://openalex.org/W2169067611","https://openalex.org/W2584620251","https://openalex.org/W2605350416","https://openalex.org/W2788402790","https://openalex.org/W2788816357","https://openalex.org/W2796608345","https://openalex.org/W2808408933","https://openalex.org/W2903718012","https://openalex.org/W2907492528","https://openalex.org/W2964159782","https://openalex.org/W2964732194","https://openalex.org/W2965115497","https://openalex.org/W2970574682","https://openalex.org/W2972209102","https://openalex.org/W2986176093","https://openalex.org/W2996858319","https://openalex.org/W3012644407","https://openalex.org/W3025110576","https://openalex.org/W3026640598","https://openalex.org/W3035637689","https://openalex.org/W3082556740","https://openalex.org/W3114233038","https://openalex.org/W3197089777","https://openalex.org/W4206453098","https://openalex.org/W4206471589","https://openalex.org/W4233762729","https://openalex.org/W4297971002","https://openalex.org/W4306316988","https://openalex.org/W4306317202","https://openalex.org/W4312249945","https://openalex.org/W4312842549","https://openalex.org/W4382203112","https://openalex.org/W4385482601","https://openalex.org/W4385484879","https://openalex.org/W4385489021","https://openalex.org/W4386514492","https://openalex.org/W4387846272"],"related_works":[],"abstract_inverted_index":{"Cross-network":[0],"link":[1,157],"prediction":[2],"aims":[3],"to":[4,79,110,129,137,152,177],"predict":[5],"the":[6,41,54,58,127,138,143,179],"existence":[7],"of":[8,44,126,133,181],"edges":[9],"between":[10,47,56,63,83,118],"nodes":[11,84],"in":[12,34,39,71],"a":[13,20,35,148],"target":[14],"network":[15,22,151],"by":[16],"transferring":[17],"knowledge":[18,94,169],"from":[19,85],"source":[21],"with":[23,185],"sufficient":[24],"annotations.":[25],"Existing":[26],"methods":[27],"mainly":[28],"focus":[29],"on":[30,97,142,174],"associating":[31],"two":[32,48,119],"networks":[33,49,135],"shared":[36],"embedding":[37],"space,":[38],"which":[40,65],"distribution":[42],"discrepancy":[43,55,60],"node":[45,116,154],"embeddings":[46,155],"is":[50],"reduced.":[51],"However,":[52],"besides":[53],"embeddings,":[57],"structure":[59,112],"also":[61],"appears":[62],"networks,":[64,87,120],"has":[66],"not":[67],"been":[68],"well":[69],"investigated":[70],"existing":[72],"studies.":[73],"In":[74],"this":[75],"paper,":[76],"we":[77,104,146],"propose":[78,105],"learn":[80,153],"cross-network":[81],"relationship":[82],"different":[86],"and":[88,121,163],"construct":[89],"an":[90,106],"augmented":[91],"graph":[92,128,149],"for":[93,114,156,168],"transfer":[95],"based":[96],"our":[98,182],"learned":[99],"relationship.":[100,140],"To":[101],"achieve":[102],"this,":[103],"optimal":[107],"transport":[108],"model":[109],"exploit":[111],"information":[113,132,165],"learning":[115],"association":[117],"then":[122],"apply":[123],"barycentric":[124],"mapping":[125],"combine":[130],"structural":[131,164],"both":[134,161],"according":[136],"obtained":[139],"Based":[141],"constructed":[144],"graph,":[145],"design":[147],"convolutional":[150],"prediction,":[158],"so":[159],"that":[160],"feature":[162],"are":[166],"leveraged":[167],"transfer.":[170],"We":[171],"conduct":[172],"experiments":[173],"benchmark":[175],"datasets":[176],"show":[178],"superiority":[180],"method":[183],"compared":[184],"state-of-the-art":[186],"methods.":[187]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
