{"id":"https://openalex.org/W7090291711","doi":"https://doi.org/10.1109/icmlt65785.2025.11192859","title":"Investigating Transfer Learning for Link Prediction in Graph Neural Networks","display_name":"Investigating Transfer Learning for Link Prediction in Graph Neural Networks","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W7090291711","doi":"https://doi.org/10.1109/icmlt65785.2025.11192859"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11192859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11192859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Mandelz Thomas","orcid":null},"institutions":[{"id":"https://openalex.org/I2972652528","display_name":"FHNW University of Applied Sciences and Arts Northwestern Switzerland","ror":"https://ror.org/04mq2g308","country_code":"CH","type":"education","lineage":["https://openalex.org/I2972652528"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Mandelz Thomas","raw_affiliation_strings":["Institute for Data Science FHNW,Windisch,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science FHNW,Windisch,Switzerland","institution_ids":["https://openalex.org/I2972652528"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zwicky Jan","orcid":null},"institutions":[{"id":"https://openalex.org/I2972652528","display_name":"FHNW University of Applied Sciences and Arts Northwestern Switzerland","ror":"https://ror.org/04mq2g308","country_code":"CH","type":"education","lineage":["https://openalex.org/I2972652528"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Zwicky Jan","raw_affiliation_strings":["Institute for Data Science FHNW,Windisch,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science FHNW,Windisch,Switzerland","institution_ids":["https://openalex.org/I2972652528"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Perruchoud Daniel","orcid":null},"institutions":[{"id":"https://openalex.org/I2972652528","display_name":"FHNW University of Applied Sciences and Arts Northwestern Switzerland","ror":"https://ror.org/04mq2g308","country_code":"CH","type":"education","lineage":["https://openalex.org/I2972652528"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Perruchoud Daniel","raw_affiliation_strings":["Institute for Data Science FHNW,Windisch,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science FHNW,Windisch,Switzerland","institution_ids":["https://openalex.org/I2972652528"]}]},{"author_position":"last","author":{"id":null,"display_name":"Heule Stephan","orcid":null},"institutions":[{"id":"https://openalex.org/I2972652528","display_name":"FHNW University of Applied Sciences and Arts Northwestern Switzerland","ror":"https://ror.org/04mq2g308","country_code":"CH","type":"education","lineage":["https://openalex.org/I2972652528"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Heule Stephan","raw_affiliation_strings":["Institute for Data Science FHNW,Windisch,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science FHNW,Windisch,Switzerland","institution_ids":["https://openalex.org/I2972652528"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2972652528"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.57041882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"176","last_page":"181"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9897000193595886,"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":0.9897000193595886,"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":0.004100000020116568,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.00139999995008111,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.7264999747276306},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6772000193595886},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5507000088691711},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5242000222206116},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.45339998602867126},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.44940000772476196},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43950000405311584},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4203999936580658}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7335000038146973},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.7264999747276306},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6772000193595886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6603999733924866},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6373000144958496},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5242000222206116},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43950000405311584},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4203999936580658},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28369998931884766},{"id":"https://openalex.org/C2777938197","wikidata":"https://www.wikidata.org/wiki/Q7834022","display_name":"Transfer of training","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.26579999923706055},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11192859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11192859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2420733993","https://openalex.org/W2945827377","https://openalex.org/W3080997787","https://openalex.org/W4210972068","https://openalex.org/W4392309180","https://openalex.org/W4394597734"],"related_works":[],"abstract_inverted_index":{"Graph":[0,97,103],"Neural":[1],"Networks":[2],"(GNNs)":[3],"emerged":[4],"as":[5,82,84],"powerful":[6],"tools":[7],"for":[8,190,193,196,207],"learning":[9,30,37,71,114,172],"representations":[10],"of":[11,25,69,112],"graph-structured":[12],"data,":[13],"increasingly":[14],"applied":[15,53],"to":[16,145,155,182,204],"various":[17],"domains.":[18],"Despite":[19],"their":[20,57],"growing":[21],"popularity,":[22],"the":[23,67,110,138,146,157,183],"transferability":[24,58,158],"GNNs":[26,46],"remains":[27],"underexplored.":[28],"Transfer":[29],"showed":[31],"remarkable":[32],"success":[33],"in":[34,54,59,72,180],"traditional":[35],"deep":[36],"tasks,":[38],"enabling":[39],"faster":[40],"training":[41,85,162,166,201],"and":[42,50,102,108,117,126,135,150],"enhanced":[43],"performance.":[44],"Although":[45],"are":[47,51,153],"gaining":[48],"popularity":[49],"being":[52],"many":[55],"areas,":[56],"link":[60,73],"prediction":[61,74],"is":[62],"not":[63],"well-studied.This":[64],"research":[65],"investigates":[66],"applications":[68],"transfer":[70,113,171],"using":[75],"GNNs,":[76],"focusing":[77],"on":[78,120,124,137],"enhancing":[79],"model":[80,175],"performance":[81,152],"well":[83],"efficiency":[86],"through":[87],"pre-training":[88,116],"GNN":[89],"models,":[90,130,160],"followed":[91],"by":[92,115],"fine-tuning.":[93],"Specifically,":[94],"we":[95],"train":[96],"Convolutional":[98],"Network":[99,105],"(GCN),":[100],"GraphSAGE":[101],"Isomorphism":[104],"(GIN)":[106],"architectures":[107],"investigate":[109],"benefits":[111],"fine-tuning":[118],"models":[119,185],"public":[121],"data":[122],"(i.e.":[123],"ogbn-papers100M":[125],"ogbn-arxiv":[127],"datasets).":[128],"Reference":[129],"constructed":[131],"with":[132],"identical":[133],"capacity":[134],"trained":[136],"same":[139],"datasets,":[140],"ensure":[141],"a":[142],"fair":[143],"comparison":[144],"fine-tuned":[147,174],"models.":[148],"Jumpstart":[149],"asymptotic":[151],"used":[154],"determine":[156],"between":[159],"while":[161,198],"time":[163,202],"ratios":[164],"measure":[165],"efficiency.Our":[167],"findings":[168],"show":[169],"that":[170],"improves":[173],"performance,":[176],"boosting":[177],"jumpstart":[178],"scores":[179],"relation":[181],"reference":[184],"range":[186],"from":[187],"0.63":[188],"(jumpstart)":[189],"GCN,":[191],"0.47":[192],"GraphSAGE,":[194],"0.48":[195],"GIN,":[197],"also":[199],"reducing":[200],"up":[203],"15":[205],"times":[206],"GraphSAGE.":[208]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-14T00:00:00"}
