Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs
<p dir="ltr">Detecting anomalies in dynamic graphs is a complex yet essential task, as existing methods often fail to capture long-term dependencies required for identifying irregularities in evolving networks. We introduce Temporal Structural Graph Anomaly Detection (T-StructGAD), a...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| _version_ | 1864513531589689344 |
|---|---|
| author | Samir Abdaljalil (11513178) |
| author2 | Hasan Kurban (13144983) Rachad Atat (16864194) Erchin Serpedin (3706543) Khalid Qaraqe (16896504) |
| author2_role | author author author author |
| author_facet | Samir Abdaljalil (11513178) Hasan Kurban (13144983) Rachad Atat (16864194) Erchin Serpedin (3706543) Khalid Qaraqe (16896504) |
| author_role | author |
| dc.creator.none.fl_str_mv | Samir Abdaljalil (11513178) Hasan Kurban (13144983) Rachad Atat (16864194) Erchin Serpedin (3706543) Khalid Qaraqe (16896504) |
| dc.date.none.fl_str_mv | 2025-07-25T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcs.2025.3584942 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_Temporal_and_Structural_Embeddings_for_Robust_Unsupervised_Anomaly_Detection_in_Dynamic_Graphs/30860093 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Anomaly detection deep learning graph neural networks dynamic graphs node embedding spatial-temporal dependencies Long short term memory Logic gates Vectors Deep learning Autoencoders Image edge detection Gated recurrent units Feature extraction Computer architecture |
| dc.title.none.fl_str_mv | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Detecting anomalies in dynamic graphs is a complex yet essential task, as existing methods often fail to capture long-term dependencies required for identifying irregularities in evolving networks. We introduce Temporal Structural Graph Anomaly Detection (T-StructGAD), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (GConvGRUs) and Long Short-Term Memory networks (LSTMs) to jointly model both structural and temporal dynamics in graph node embeddings. Anomalies are detected using reconstruction errors generated by an AutoEncoder, enabling the framework to robustly uncover deviations across time. Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. Comprehensive evaluations on four real-world datasets demonstrate that T-StructGAD consistently outperforms 12 state-of-the-art unsupervised anomaly detection models, showcasing its superior ability to detect complex anomalies in evolving graphs. This work advances anomaly detection in dynamic graphs by integrating deep learning techniques to address structural and temporal irregularities in a more effective manner.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Computer Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcs.2025.3584942" target="_blank">https://dx.doi.org/10.1109/ojcs.2025.3584942</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8efdd0c8fa389428896d93b17c2f2341 |
| identifier_str_mv | 10.1109/ojcs.2025.3584942 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30860093 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic GraphsSamir Abdaljalil (11513178)Hasan Kurban (13144983)Rachad Atat (16864194)Erchin Serpedin (3706543)Khalid Qaraqe (16896504)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningAnomaly detectiondeep learninggraph neural networksdynamic graphsnode embeddingspatial-temporal dependenciesLong short term memoryLogic gatesVectorsDeep learningAutoencodersImage edge detectionGated recurrent unitsFeature extractionComputer architecture<p dir="ltr">Detecting anomalies in dynamic graphs is a complex yet essential task, as existing methods often fail to capture long-term dependencies required for identifying irregularities in evolving networks. We introduce Temporal Structural Graph Anomaly Detection (T-StructGAD), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (GConvGRUs) and Long Short-Term Memory networks (LSTMs) to jointly model both structural and temporal dynamics in graph node embeddings. Anomalies are detected using reconstruction errors generated by an AutoEncoder, enabling the framework to robustly uncover deviations across time. Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. Comprehensive evaluations on four real-world datasets demonstrate that T-StructGAD consistently outperforms 12 state-of-the-art unsupervised anomaly detection models, showcasing its superior ability to detect complex anomalies in evolving graphs. This work advances anomaly detection in dynamic graphs by integrating deep learning techniques to address structural and temporal irregularities in a more effective manner.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Computer Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcs.2025.3584942" target="_blank">https://dx.doi.org/10.1109/ojcs.2025.3584942</a></p>2025-07-25T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcs.2025.3584942https://figshare.com/articles/journal_contribution/Deep_Temporal_and_Structural_Embeddings_for_Robust_Unsupervised_Anomaly_Detection_in_Dynamic_Graphs/30860093CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308600932025-07-25T12:00:00Z |
| spellingShingle | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs Samir Abdaljalil (11513178) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Anomaly detection deep learning graph neural networks dynamic graphs node embedding spatial-temporal dependencies Long short term memory Logic gates Vectors Deep learning Autoencoders Image edge detection Gated recurrent units Feature extraction Computer architecture |
| status_str | publishedVersion |
| title | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs |
| title_full | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs |
| title_fullStr | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs |
| title_full_unstemmed | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs |
| title_short | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs |
| title_sort | Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Anomaly detection deep learning graph neural networks dynamic graphs node embedding spatial-temporal dependencies Long short term memory Logic gates Vectors Deep learning Autoencoders Image edge detection Gated recurrent units Feature extraction Computer architecture |