Structural and node-level augmentations for graph anomalies.
<p>Left: original attributed network <i>G</i>. Right: augmented attributed network . Middle: augmentation methods: (1) feature copying (attribute mimicking across distant nodes); (2) feature scaling (multiplying or dividing continuous attributes); (3) node isolation (dropping all i...
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2025
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| _version_ | 1849927641084198912 |
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| 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 | 2025-11-24T18:37:54Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0335135.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Structural_and_node-level_augmentations_for_graph_anomalies_/30698008 |
| 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 | Structural and node-level augmentations for graph anomalies. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Left: original attributed network <i>G</i>. Right: augmented attributed network . Middle: augmentation methods: (1) feature copying (attribute mimicking across distant nodes); (2) feature scaling (multiplying or dividing continuous attributes); (3) node isolation (dropping all incident edges of selected nodes); (4) random shortcut connections and clique injection (adding shortcuts or small dense cliques across and within communities). Color coding: green nodes are normal; red nodes are augmented (selected for structure or feature augmentations; isolated nodes appear red with no incident edges); gray edges are original connections; orange or red edges indicate injected connections (random shortcuts or clique edges); solid feature bars are original attributes; cross-hatched bars mark augmented features; the thin gray curved arrow in the feature panel indicates attribute copying. Collectively, these augmentations induce structural, attribute, and interaction anomalies, creating cross-view discrepancies leveraged by our reconstruction-level contrast.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_e45233acdc4781007622c52bf5861738 |
| identifier_str_mv | 10.1371/journal.pone.0335135.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30698008 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Structural and node-level augmentations for graph anomalies.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>Left: original attributed network <i>G</i>. Right: augmented attributed network . Middle: augmentation methods: (1) feature copying (attribute mimicking across distant nodes); (2) feature scaling (multiplying or dividing continuous attributes); (3) node isolation (dropping all incident edges of selected nodes); (4) random shortcut connections and clique injection (adding shortcuts or small dense cliques across and within communities). Color coding: green nodes are normal; red nodes are augmented (selected for structure or feature augmentations; isolated nodes appear red with no incident edges); gray edges are original connections; orange or red edges indicate injected connections (random shortcuts or clique edges); solid feature bars are original attributes; cross-hatched bars mark augmented features; the thin gray curved arrow in the feature panel indicates attribute copying. Collectively, these augmentations induce structural, attribute, and interaction anomalies, creating cross-view discrepancies leveraged by our reconstruction-level contrast.</p>2025-11-24T18:37:54ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0335135.g003https://figshare.com/articles/figure/Structural_and_node-level_augmentations_for_graph_anomalies_/30698008CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306980082025-11-24T18:37:54Z |
| spellingShingle | Structural and node-level augmentations for graph anomalies. 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 | Structural and node-level augmentations for graph anomalies. |
| title_full | Structural and node-level augmentations for graph anomalies. |
| title_fullStr | Structural and node-level augmentations for graph anomalies. |
| title_full_unstemmed | Structural and node-level augmentations for graph anomalies. |
| title_short | Structural and node-level augmentations for graph anomalies. |
| title_sort | Structural and node-level augmentations for graph anomalies. |
| 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 |