Condensed summary of graph anomaly detection methods and their key strengths and limitations.
<p>Condensed summary of graph anomaly detection methods and their key strengths and limitations.</p>
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| अन्य लेखक: | , , |
| प्रकाशित: |
2025
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| विषय: | |
| टैग: |
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| _version_ | 1849927640790597632 |
|---|---|
| author | Hossein Rafieizadeh (22676722) |
| author2 | Hadi Zare (20073000) Mohsen Ghassemi Parsa (22676725) Hocine Cherifi (8177628) |
| author2_role | author author author |
| author_facet | Hossein Rafieizadeh (22676722) Hadi Zare (20073000) Mohsen Ghassemi Parsa (22676725) Hocine Cherifi (8177628) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hossein Rafieizadeh (22676722) Hadi Zare (20073000) Mohsen Ghassemi Parsa (22676725) Hocine Cherifi (8177628) |
| dc.date.none.fl_str_mv | 2025-11-24T18:38:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0335135.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Condensed_summary_of_graph_anomaly_detection_methods_and_their_key_strengths_and_limitations_/30698023 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified intrusions across social reconstructions across views level contrastive learning dual contrastive learning across six benchmarks div >< p view discrepancies underutilized augmented graph views dcor improves auroc view discrepancies level contrast dual autoencoder augmented view specific information six datasets reduces auroc publicly available preserves fine physical domains performing non maximum gain leaving cross identifying threats financial fraud existing graph dcor reconstructs dcor ), contrasts reconstructions attributed networks attribute patterns |
| dc.title.none.fl_str_mv | Condensed summary of graph anomaly detection methods and their key strengths and limitations. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Condensed summary of graph anomaly detection methods and their key strengths and limitations.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_2ea7ebe6112bdca095ae4657e250b966 |
| identifier_str_mv | 10.1371/journal.pone.0335135.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30698023 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Condensed summary of graph anomaly detection methods and their key strengths and limitations.Hossein Rafieizadeh (22676722)Hadi Zare (20073000)Mohsen Ghassemi Parsa (22676725)Hocine Cherifi (8177628)Cell BiologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedintrusions across socialreconstructions across viewslevel contrastive learningdual contrastive learningacross six benchmarksdiv >< pview discrepancies underutilizedaugmented graph viewsdcor improves aurocview discrepancieslevel contrastdual autoencoderaugmented viewspecific informationsix datasetsreduces aurocpublicly availablepreserves finephysical domainsperforming nonmaximum gainleaving crossidentifying threatsfinancial fraudexisting graphdcor reconstructsdcor ),contrasts reconstructionsattributed networksattribute patterns<p>Condensed summary of graph anomaly detection methods and their key strengths and limitations.</p>2025-11-24T18:38:00ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0335135.t002https://figshare.com/articles/dataset/Condensed_summary_of_graph_anomaly_detection_methods_and_their_key_strengths_and_limitations_/30698023CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306980232025-11-24T18:38:00Z |
| spellingShingle | Condensed summary of graph anomaly detection methods and their key strengths and limitations. Hossein Rafieizadeh (22676722) Cell Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified intrusions across social reconstructions across views level contrastive learning dual contrastive learning across six benchmarks div >< p view discrepancies underutilized augmented graph views dcor improves auroc view discrepancies level contrast dual autoencoder augmented view specific information six datasets reduces auroc publicly available preserves fine physical domains performing non maximum gain leaving cross identifying threats financial fraud existing graph dcor reconstructs dcor ), contrasts reconstructions attributed networks attribute patterns |
| status_str | publishedVersion |
| title | Condensed summary of graph anomaly detection methods and their key strengths and limitations. |
| title_full | Condensed summary of graph anomaly detection methods and their key strengths and limitations. |
| title_fullStr | Condensed summary of graph anomaly detection methods and their key strengths and limitations. |
| title_full_unstemmed | Condensed summary of graph anomaly detection methods and their key strengths and limitations. |
| title_short | Condensed summary of graph anomaly detection methods and their key strengths and limitations. |
| title_sort | Condensed summary of graph anomaly detection methods and their key strengths and limitations. |
| topic | Cell Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified intrusions across social reconstructions across views level contrastive learning dual contrastive learning across six benchmarks div >< p view discrepancies underutilized augmented graph views dcor improves auroc view discrepancies level contrast dual autoencoder augmented view specific information six datasets reduces auroc publicly available preserves fine physical domains performing non maximum gain leaving cross identifying threats financial fraud existing graph dcor reconstructs dcor ), contrasts reconstructions attributed networks attribute patterns |