{"id":"https://openalex.org/W4404037695","doi":"https://doi.org/10.1109/mlsp58920.2024.10734779","title":"DualGCN: A Convolutional Network for Anomaly Detection in Directed Graphs Based on Symmetrized and Skew-Symmetrized Laplacians","display_name":"DualGCN: A Convolutional Network for Anomaly Detection in Directed Graphs Based on Symmetrized and Skew-Symmetrized Laplacians","publication_year":2024,"publication_date":"2024-09-22","ids":{"openalex":"https://openalex.org/W4404037695","doi":"https://doi.org/10.1109/mlsp58920.2024.10734779"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp58920.2024.10734779","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp58920.2024.10734779","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5113030574","display_name":"Theodor\u2013Adrian Badea","orcid":null},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Theodor-Adrian Badea","raw_affiliation_strings":["National University of Science and Technology, Politehnica Bucharest,Department of Automatic Control and Computers,Bucharest,Romania,060042"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Science and Technology, Politehnica Bucharest,Department of Automatic Control and Computers,Bucharest,Romania,060042","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023994037","display_name":"Bogdan Dumitrescu","orcid":"https://orcid.org/0000-0003-4555-1714"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Bogdan Dumitrescu","raw_affiliation_strings":["National University of Science and Technology, Politehnica Bucharest,Department of Automatic Control and Computers,Bucharest,Romania,060042"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Science and Technology, Politehnica Bucharest,Department of Automatic Control and Computers,Bucharest,Romania,060042","institution_ids":["https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3055,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66255334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9664999842643738,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9491000175476074,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.8567668199539185},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5608766078948975},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.48352503776550293},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4346049427986145},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35486939549446106},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2787089943885803},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.15203014016151428},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06607747077941895}],"concepts":[{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.8567668199539185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5608766078948975},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.48352503776550293},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4346049427986145},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35486939549446106},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2787089943885803},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15203014016151428},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06607747077941895},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp58920.2024.10734779","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp58920.2024.10734779","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G631419740","display_name":null,"funder_award_id":"PN-III-P4-PCE-2021-0154","funder_id":"https://openalex.org/F4320308292","funder_display_name":"Corporation for National and Community Service"}],"funders":[{"id":"https://openalex.org/F4320308292","display_name":"Corporation for National and Community Service","ror":"https://ror.org/00p8rjs69"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2158787690","https://openalex.org/W2296719434","https://openalex.org/W2907417050","https://openalex.org/W2907492528","https://openalex.org/W2962844340","https://openalex.org/W2964015378","https://openalex.org/W2964321699","https://openalex.org/W2981450816","https://openalex.org/W3022222974","https://openalex.org/W3169020476","https://openalex.org/W4226383825","https://openalex.org/W4387914174","https://openalex.org/W4389364114","https://openalex.org/W6713582119","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6757312246","https://openalex.org/W6765782556","https://openalex.org/W6776830747","https://openalex.org/W6796184174","https://openalex.org/W6842545896","https://openalex.org/W6859746055"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969"],"abstract_inverted_index":{"Graph":[0],"machine":[1],"learning":[2],"techniques":[3],"and":[4,61,96,114],"notably":[5],"graph":[6,34],"neural":[7],"networks":[8,36],"(GNNs)":[9],"have":[10],"seen":[11],"a":[12,29,88],"surge":[13],"in":[14,28,53],"popularity":[15],"due":[16],"to":[17,40,46,73,82],"the":[18,24,54,58,63,74,94,102,107],"suitability":[19],"of":[20,31,57,62,66],"graphs":[21],"for":[22,111],"being":[23,69],"underlying":[25],"data":[26,113],"structure":[27],"multitude":[30],"applications.":[32],"Spectral":[33],"convolutional":[35],"(GCNs),":[37],"however,":[38],"seem":[39],"encounter":[41],"shortfalls":[42],"when":[43],"it":[44],"comes":[45],"directed":[47],"graphs.":[48],"The":[49],"root":[50],"cause":[51],"lies":[52],"asymmetric":[55],"nature":[56],"adjacency":[59],"matrices":[60],"resulting":[64],"Laplacians":[65],"such":[67],"graphs;":[68],"ill-behaved":[70],"with":[71,119],"respect":[72],"spectral":[75,89],"theory.":[76],"In":[77],"this":[78,84],"paper,":[79],"we":[80,105],"attempt":[81],"overcome":[83],"limitation":[85],"through":[86],"DualGCN:":[87],"GCN":[90],"approach":[91],"based":[92],"on":[93],"symmetrized":[95],"skew-symmetrized":[97],"Laplacian":[98],"matrices.":[99],"To":[100],"test":[101],"proposed":[103],"method,":[104],"undertake":[106],"anomaly":[108],"detection":[109],"problem":[110],"graph-structured":[112],"obtain":[115],"better":[116],"results":[117],"than":[118],"other":[120],"approaches.":[121]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}