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In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and ...
In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and ...
Oct 4, 2022 · More recently, graph neural networks (GNNs) have been adopted to efficiently and intuitively detect anomalies from graphs due to the highly ...
ABSTRACT. The applications of Generative Adversarial Networks (GANs) are just as diverse as their architectures, problem settings as well as challenges.
Apart from data-specific and task-specific issues, it is also challenging to apply the graph neural network directly to anomaly detection task sdue to its ...
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Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important ...
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May 20, 2024 · Keywords: Graph neural networks, Adversarial Attack, Robustness Detection ... Rethinking graph neural networks for anomaly detection. In ...
Graph generative adversarial networks can be used in dynamic graph anomaly detection due to their ability to model complex data, but the original graph ...
A collection of papers on deep learning for graph anomaly detection, and published algorithms and datasets.
Abstract. Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks?