{"id":"https://openalex.org/W7128717998","doi":"https://doi.org/10.48550/arxiv.2602.10754","title":"Exploring the impact of adaptive rewiring in Graph Neural Networks","display_name":"Exploring the impact of adaptive rewiring in Graph Neural Networks","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128717998","doi":"https://doi.org/10.48550/arxiv.2602.10754"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.10754","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092050475","display_name":"Charlotte Cambier van Nooten","orcid":"https://orcid.org/0000-0001-8057-7333"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"van Nooten, Charlotte Cambier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125748605","display_name":"Christos Aronis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aronis, Christos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125734772","display_name":"Yuliya Shapovalova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shapovalova, Yuliya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086236981","display_name":"Lucia Cavallaro","orcid":"https://orcid.org/0000-0002-2367-6084"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cavallaro, Lucia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9114000201225281,"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":0.9114000201225281,"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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.00559999980032444,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.004699999932199717,"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/graph","display_name":"Graph","score":0.4708999991416931},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.42579999566078186},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.3993000090122223},{"id":"https://openalex.org/keywords/graph-isomorphism","display_name":"Graph isomorphism","score":0.3928000032901764},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.38920000195503235},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3822000026702881},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.34360000491142273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746999979019165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4812999963760376},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47760000824928284},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4708999991416931},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3984000086784363},{"id":"https://openalex.org/C61665672","wikidata":"https://www.wikidata.org/wiki/Q303100","display_name":"Graph isomorphism","level":4,"score":0.3928000032901764},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3822000026702881},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.3255999982357025},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.29820001125335693},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.10754","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.10754","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10754","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.10754","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4864073395729065}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,159],"paper":[1],"explores":[2],"sparsification":[3,91,96],"methods":[4,70],"as":[5],"a":[6,60],"form":[7],"of":[8,44,74,90,101,119,133],"regularization":[9],"in":[10,23,57,170],"Graph":[11,78,83],"Neural":[12],"Networks":[13,80,85],"(GNNs)":[14],"to":[15,71,108,152,162],"address":[16],"high":[17],"memory":[18],"usage":[19],"and":[20,32,82,92,112],"computational":[21],"costs":[22],"large-scale":[24],"graph":[25],"applications.":[26,48],"Using":[27],"techniques":[28],"from":[29,104],"Network":[30],"Science":[31],"Machine":[33],"Learning,":[34],"including":[35],"Erd\u0151s-R\u00e9nyi":[36],"for":[37,46,63,172],"model":[38,151],"sparsification,":[39],"we":[40],"enhance":[41],"the":[42,99,117,150],"efficiency":[43],"GNNs":[45,171],"real-world":[47],"We":[49,67],"demonstrate":[50],"our":[51,69],"approach":[52],"on":[53],"N-1":[54],"contingency":[55],"assessment":[56],"electrical":[58],"grids,":[59],"critical":[61,173],"task":[62],"ensuring":[64],"grid":[65,177],"reliability.":[66],"apply":[68],"three":[72],"datasets":[73],"varying":[75],"sizes,":[76],"exploring":[77],"Convolutional":[79],"(GCN)":[81],"Isomorphism":[84],"(GIN)":[86],"with":[87,143],"different":[88],"degrees":[89],"rewiring.":[93],"Comparison":[94],"across":[95],"levels":[97],"shows":[98],"potential":[100],"combining":[102],"insights":[103],"both":[105],"research":[106,160],"fields":[107],"improve":[109,126],"GNN":[110],"performance":[111],"scalability.":[113],"Our":[114,136],"experiments":[115],"highlight":[116],"importance":[118],"tuning":[120],"sparsity":[121,124,129,165],"parameters:":[122],"while":[123],"can":[125,166],"generalization,":[127],"excessive":[128],"may":[130],"hinder":[131],"learning":[132],"complex":[134],"patterns.":[135],"adaptive":[137],"rewiring":[138],"approach,":[139],"particularly":[140],"when":[141],"combined":[142],"early":[144],"stopping,":[145],"proves":[146],"promising":[147],"by":[148],"allowing":[149],"adapt":[153],"its":[154],"connectivity":[155],"structure":[156],"during":[157],"training.":[158],"contributes":[161],"understanding":[163],"how":[164],"be":[167],"effectively":[168],"leveraged":[169],"applications":[174],"like":[175],"power":[176],"reliability":[178],"analysis.":[179]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-13T00:00:00"}