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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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Samir Abdaljalil (11513178) (author)
مؤلفون آخرون:	Hasan Kurban (13144983) (author), Rachad Atat (16864194) (author), Erchin Serpedin (3706543) (author), Khalid Qaraqe (16896504) (author)
منشور في:	2025
الموضوعات:	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
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Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs

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