{"id":"https://openalex.org/W4399860528","doi":"https://doi.org/10.1145/3661725.3661784","title":"Sequencial Event Graph Mining in Power Grid Accident Tracing Based on RED-GNN Algorithm","display_name":"Sequencial Event Graph Mining in Power Grid Accident Tracing Based on RED-GNN Algorithm","publication_year":2024,"publication_date":"2024-04-12","ids":{"openalex":"https://openalex.org/W4399860528","doi":"https://doi.org/10.1145/3661725.3661784"},"language":"en","primary_location":{"id":"doi:10.1145/3661725.3661784","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3661725.3661784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Computing Machine Learning and Data Science","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":"https://openalex.org/A5068570957","display_name":"Xinsheng Chen","orcid":"https://orcid.org/0000-0002-4616-6300"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinsheng Chen","raw_affiliation_strings":["State Grid Shanghai Municipal Electric Power Company, China"],"raw_orcid":"https://orcid.org/0000-0002-4616-6300","affiliations":[{"raw_affiliation_string":"State Grid Shanghai Municipal Electric Power Company, China","institution_ids":["https://openalex.org/I4210126065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018632456","display_name":"Xiaofa Zhou","orcid":"https://orcid.org/0000-0002-5735-7815"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofa Zhou","raw_affiliation_strings":["State Grid Shanghai Municipal Electric Power Company, China"],"raw_orcid":"https://orcid.org/0000-0002-5735-7815","affiliations":[{"raw_affiliation_string":"State Grid Shanghai Municipal Electric Power Company, China","institution_ids":["https://openalex.org/I4210126065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103243690","display_name":"Jin Pan","orcid":"https://orcid.org/0009-0001-9388-361X"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Pan","raw_affiliation_strings":["State Grid Shanghai Municipal Electric Power Company, China"],"raw_orcid":"https://orcid.org/0009-0001-9388-361X","affiliations":[{"raw_affiliation_string":"State Grid Shanghai Municipal Electric Power Company, China","institution_ids":["https://openalex.org/I4210126065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029893225","display_name":"Xin Qiu","orcid":"https://orcid.org/0009-0004-3763-1372"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Qiu","raw_affiliation_strings":["State Grid Shanghai Municipal Electric Power Company, China"],"raw_orcid":"https://orcid.org/0009-0004-3763-1372","affiliations":[{"raw_affiliation_string":"State Grid Shanghai Municipal Electric Power Company, China","institution_ids":["https://openalex.org/I4210126065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210126065"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11357","display_name":"Risk and Safety Analysis","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11357","display_name":"Risk and Safety Analysis","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9778000116348267,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9692999720573425,"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/tracing","display_name":"Tracing","score":0.8134020566940308},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7331657409667969},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6017839312553406},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5871180295944214},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5691376328468323},{"id":"https://openalex.org/keywords/power-grid","display_name":"Power grid","score":0.5327800512313843},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5151061415672302},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43609148263931274},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3209133744239807},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.31802064180374146},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2714393138885498},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10550668835639954}],"concepts":[{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.8134020566940308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7331657409667969},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6017839312553406},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5871180295944214},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5691376328468323},{"id":"https://openalex.org/C2983254600","wikidata":"https://www.wikidata.org/wiki/Q1096907","display_name":"Power grid","level":3,"score":0.5327800512313843},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5151061415672302},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43609148263931274},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3209133744239807},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.31802064180374146},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2714393138885498},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10550668835639954},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3661725.3661784","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3661725.3661784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Computing Machine Learning and Data Science","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":22,"referenced_works":["https://openalex.org/W1666929998","https://openalex.org/W1817561967","https://openalex.org/W1999207031","https://openalex.org/W2088037931","https://openalex.org/W2109574129","https://openalex.org/W2131674589","https://openalex.org/W2170083989","https://openalex.org/W2343811890","https://openalex.org/W2541577353","https://openalex.org/W2759136286","https://openalex.org/W2780265514","https://openalex.org/W2909230076","https://openalex.org/W2940593286","https://openalex.org/W2946539590","https://openalex.org/W3179205840","https://openalex.org/W4200570604","https://openalex.org/W4280570454","https://openalex.org/W4296219853","https://openalex.org/W4311046638","https://openalex.org/W4320516263","https://openalex.org/W6629807868","https://openalex.org/W6736685754"],"related_works":["https://openalex.org/W2888673113","https://openalex.org/W2212288070","https://openalex.org/W2056065966","https://openalex.org/W2352602608","https://openalex.org/W2062641654","https://openalex.org/W3149975758","https://openalex.org/W1517786189","https://openalex.org/W3213987435","https://openalex.org/W3023262859","https://openalex.org/W2132420969"],"abstract_inverted_index":{"In":[0],"the":[1,8,39,45,55,63,90,118,122,127],"complex":[2],"arena":[3],"of":[4,12,41,57,66,94,129],"power":[5,31,56,99,109],"grid":[6,100,110],"management,":[7],"tracing":[9,131],"and":[10,19,92,117],"analysis":[11,29,113],"accidents":[13],"are":[14],"pivotal":[15],"for":[16,112],"ensuring":[17],"safety":[18],"reliability.":[20],"This":[21],"paper":[22],"introduces":[23],"an":[24],"innovative":[25],"approach":[26],"to":[27,61,69],"accident":[28,86,130],"in":[30,85,98],"grids":[32],"using":[33],"Sequential":[34],"Event":[35],"Graph":[36,49],"Mining.":[37],"At":[38],"core":[40],"this":[42,79],"method":[43,80],"is":[44],"RED-GNN":[46,123],"(Recurrent":[47],"Event-Driven":[48],"Convolutional":[50],"Network)":[51],"algorithm,":[52],"which":[53],"leverages":[54],"graph-based":[58],"deep":[59,77],"learning":[60],"unravel":[62],"intricate":[64],"sequences":[65,75],"events":[67],"leading":[68],"accidents.":[70],"By":[71],"integrating":[72],"event":[73],"graph":[74],"with":[76],"learning,":[78],"offers":[81],"a":[82],"novel":[83],"perspective":[84],"tracing,":[87],"significantly":[88],"enhancing":[89],"accuracy":[91,128],"efficiency":[93],"identifying":[95],"root":[96],"causes":[97],"incidents.":[101],"The":[102],"experiment":[103],"used":[104],"real":[105],"data":[106],"from":[107],"one":[108],"company":[111],"over":[114,132],"three":[115],"years,":[116],"results":[119],"showed":[120],"that":[121],"algorithm":[124],"can":[125],"improve":[126],"4%.":[133]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}