{"id":"https://openalex.org/W4404037558","doi":"https://doi.org/10.1109/allerton63246.2024.10735180","title":"Transferable Learning of GCN Sampling Graph Data Clusters from Different Power Systems","display_name":"Transferable Learning of GCN Sampling Graph Data Clusters from Different Power Systems","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4404037558","doi":"https://doi.org/10.1109/allerton63246.2024.10735180"},"language":"en","primary_location":{"id":"doi:10.1109/allerton63246.2024.10735180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton63246.2024.10735180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 60th Annual Allerton Conference on Communication, Control, and Computing","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/A5112858390","display_name":"Tong Wu","orcid":"https://orcid.org/0009-0001-0472-5178"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tong Wu","raw_affiliation_strings":["University of Central Florida,The Department of Electrical and Computer Engi-neering,Orlando,FL,USA,32816"],"affiliations":[{"raw_affiliation_string":"University of Central Florida,The Department of Electrical and Computer Engi-neering,Orlando,FL,USA,32816","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029881017","display_name":"Anna Scaglione","orcid":"https://orcid.org/0000-0002-8892-3680"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anna Scaglione","raw_affiliation_strings":["Cornell University,The Department of Electrical and Computer Engineering, Cornell Tech,New York City,NY,USA,10044"],"affiliations":[{"raw_affiliation_string":"Cornell University,The Department of Electrical and Computer Engineering, Cornell Tech,New York City,NY,USA,10044","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019549039","display_name":"Daniel Arnold","orcid":"https://orcid.org/0000-0003-4983-2388"},"institutions":[{"id":"https://openalex.org/I148283060","display_name":"Lawrence Berkeley National Laboratory","ror":"https://ror.org/02jbv0t02","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I148283060","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Arnold","raw_affiliation_strings":["Lawrence Berkeley National Laboratory"],"affiliations":[{"raw_affiliation_string":"Lawrence Berkeley National Laboratory","institution_ids":["https://openalex.org/I148283060"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100783476","display_name":"Tianyi Chen","orcid":"https://orcid.org/0000-0003-3477-1439"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Chen","raw_affiliation_strings":["Computer and Systems Engineering, Rensselaer Polytechnic Institute,The Department of Electrical,Troy,NY,USA,12180"],"affiliations":[{"raw_affiliation_string":"Computer and Systems Engineering, Rensselaer Polytechnic Institute,The Department of Electrical,Troy,NY,USA,12180","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112858390"],"corresponding_institution_ids":["https://openalex.org/I106165777"],"apc_list":null,"apc_paid":null,"fwci":0.5246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67715276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9251999855041504,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9251999855041504,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9096999764442444,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7054829001426697},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4840478301048279},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4356302320957184},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.30078834295272827},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11329284310340881}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7054829001426697},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4840478301048279},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4356302320957184},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30078834295272827},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11329284310340881},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/allerton63246.2024.10735180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton63246.2024.10735180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 60th Annual Allerton Conference on Communication, Control, and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5712968517","display_name":null,"funder_award_id":"NSF ECCS # 2210012","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2906632784","https://openalex.org/W3000586467","https://openalex.org/W3005548505","https://openalex.org/W3013246316","https://openalex.org/W3041436169","https://openalex.org/W3107270903","https://openalex.org/W3135258945","https://openalex.org/W3217296435","https://openalex.org/W4306882174","https://openalex.org/W4308216324","https://openalex.org/W4319988721","https://openalex.org/W4388676655","https://openalex.org/W4391454475","https://openalex.org/W4394862604","https://openalex.org/W4401880112","https://openalex.org/W6803471971"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Contemporary":[0],"neural":[1],"network":[2],"(NN)":[3],"detectors":[4,21],"for":[5,106,141],"power":[6,14,35,84,99,156,170],"systems":[7,85],"face":[8],"two":[9],"primary":[10],"challenges.":[11],"First,":[12],"each":[13,70],"system":[15],"requires":[16],"individual":[17],"training":[18,79],"of":[19,41,94],"NN":[20],"to":[22,81,138],"accommodate":[23],"its":[24,73],"unique":[25],"configuration":[26],"and":[27,72,86,115],"base":[28],"demands.":[29],"Second,":[30],"significant":[31],"changes":[32],"within":[33],"the":[34,39,58,78,92,95,124,142,146,164],"system,":[36],"such":[37,109],"as":[38,110],"introduction":[40],"new":[42,45],"substations":[43],"or":[44],"generators,":[46],"necessitate":[47],"retraining.":[48],"To":[49],"overcome":[50],"these":[51],"issues,":[52],"we":[53],"introduce":[54],"a":[55,168],"novel":[56],"architecture,":[57],"Nodal":[59],"Graph":[60],"Convolutional":[61],"Neural":[62],"Network":[63],"(NGCN),":[64],"which":[65,149],"utilizes":[66],"graph":[67],"convolutions":[68],"at":[69],"bus":[71],"neighborhoods.":[74],"This":[75],"approach":[76],"allows":[77],"process":[80],"encompass":[82],"multiple":[83,155],"include":[87],"all":[88],"buses,":[89],"thereby":[90],"enhancing":[91],"transferability":[93],"method":[96],"across":[97],"different":[98],"systems.":[100],"The":[101],"NGCN":[102,125,165],"is":[103,150],"particularly":[104],"effective":[105],"detection":[107,133],"tasks,":[108],"cyber-attacks":[111],"on":[112,167],"smart":[113],"inverters":[114],"false":[116],"data":[117],"injection":[118],"attacks.":[119],"Our":[120],"tests":[121],"demonstrate":[122],"that":[123],"significantly":[126],"improves":[127],"performance":[128],"over":[129],"traditional":[130],"NNs,":[131],"boosting":[132],"accuracy":[134],"from":[135,154],"approximately":[136],"85%":[137],"around":[139],"97%":[140],"aforementioned":[143],"task.":[144],"Furthermore,":[145],"transferable":[147],"NGCN,":[148],"trained":[151,166],"by":[152],"samples":[153],"systems,":[157],"performs":[158],"considerably":[159],"better":[160],"in":[161],"evaluations":[162],"than":[163],"single":[169],"system.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
