Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs

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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Bibliographic Details
Main Author: Samir Abdaljalil (11513178) (author)
Other Authors: Hasan Kurban (13144983) (author), Rachad Atat (16864194) (author), Erchin Serpedin (3706543) (author), Khalid Qaraqe (16896504) (author)
Published: 2025
Subjects:
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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