Anomaly detection performance (AUROC).
<div><p>Anomaly detection in attributed networks is critical for identifying threats such as financial fraud and intrusions across social, e-commerce, and cyber-physical domains. Existing graph-based methods face two limitations: (i) embedding-based approaches obscure fine-grained struct...
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2025
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| _version_ | 1849927640786403328 |
|---|---|
| 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 | 2025-11-24T18:38:02Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0335135.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Anomaly_detection_performance_AUROC_/30698029 |
| 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 | Anomaly detection performance (AUROC). |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Anomaly detection in attributed networks is critical for identifying threats such as financial fraud and intrusions across social, e-commerce, and cyber-physical domains. Existing graph-based methods face two limitations: (i) embedding-based approaches obscure fine-grained structural and attribute patterns, and (ii) reconstruction-based methods neglect cross-view discrepancies during training, leaving cross-view discrepancies underutilized. To address these gaps, we propose Dual Contrastive Learning-based Reconstruction (DCOR), a dual autoencoder with a shared Graph neural network (GNN) encoder that contrasts reconstructions (not embeddings) between original and augmented graph views. Instead of contrasting embeddings, DCOR reconstructs both adjacency and attributes for the original graph and for an augmented view, then contrasts the reconstructions across views. This preserves fine-grained, view-specific information and improves the fidelity of both structure and attributes. Across six benchmarks (Enron, Amazon, Facebook, Flickr, ACM, and Reddit), DCOR achieves the best Area Under the Receiver Operating Characteristic curve (AUROC) on six datasets. In comparison with the best-performing non-DCOR baseline across datasets, DCOR improves AUROC by 11.3% on average, with a maximum gain of 21.3% on Enron. On Amazon, ablating the reconstruction-level contrast (RLC) reduces AUROC by 25.5% relative to the model, underscoring the necessity of reconstruction-level contrastive learning. Code and datasets are publicly available at <a href="https://github.com/Hossein1998/DCOR-Graph-Anomaly-Detection.git" target="_blank">https://github.com/Hossein1998/DCOR-Graph-Anomaly-Detection.git</a>.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_86881a6638696f0eb4d3dfd55576e56b |
| identifier_str_mv | 10.1371/journal.pone.0335135.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30698029 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Anomaly detection performance (AUROC).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<div><p>Anomaly detection in attributed networks is critical for identifying threats such as financial fraud and intrusions across social, e-commerce, and cyber-physical domains. Existing graph-based methods face two limitations: (i) embedding-based approaches obscure fine-grained structural and attribute patterns, and (ii) reconstruction-based methods neglect cross-view discrepancies during training, leaving cross-view discrepancies underutilized. To address these gaps, we propose Dual Contrastive Learning-based Reconstruction (DCOR), a dual autoencoder with a shared Graph neural network (GNN) encoder that contrasts reconstructions (not embeddings) between original and augmented graph views. Instead of contrasting embeddings, DCOR reconstructs both adjacency and attributes for the original graph and for an augmented view, then contrasts the reconstructions across views. This preserves fine-grained, view-specific information and improves the fidelity of both structure and attributes. Across six benchmarks (Enron, Amazon, Facebook, Flickr, ACM, and Reddit), DCOR achieves the best Area Under the Receiver Operating Characteristic curve (AUROC) on six datasets. In comparison with the best-performing non-DCOR baseline across datasets, DCOR improves AUROC by 11.3% on average, with a maximum gain of 21.3% on Enron. On Amazon, ablating the reconstruction-level contrast (RLC) reduces AUROC by 25.5% relative to the model, underscoring the necessity of reconstruction-level contrastive learning. Code and datasets are publicly available at <a href="https://github.com/Hossein1998/DCOR-Graph-Anomaly-Detection.git" target="_blank">https://github.com/Hossein1998/DCOR-Graph-Anomaly-Detection.git</a>.</p></div>2025-11-24T18:38:02ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0335135.t004https://figshare.com/articles/dataset/Anomaly_detection_performance_AUROC_/30698029CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306980292025-11-24T18:38:02Z |
| spellingShingle | Anomaly detection performance (AUROC). 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 | Anomaly detection performance (AUROC). |
| title_full | Anomaly detection performance (AUROC). |
| title_fullStr | Anomaly detection performance (AUROC). |
| title_full_unstemmed | Anomaly detection performance (AUROC). |
| title_short | Anomaly detection performance (AUROC). |
| title_sort | Anomaly detection performance (AUROC). |
| 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 |