However, a major concern is their robustness, particularly when faced with graph data that has been deliberately or accidentally polluted with noise. This presents a challenge in learning robust ...

To address these limitations, we integrate Kolmogorov-Arnold Networks (KANs), independent subspaces, and collaborative decoding techniques into the masked graph autoencoder (Mask ... model’s ...

[paper] [code] [Fan2020] ANOMALYDAE: Dual Autoencoder for Anomaly Detection ... Local Useful Information for Attribute Graph Anomaly Detection in Neurocomputing, 2024. [paper] [Xi2024] NMFAD: Neighbor ...

However, existing works often overemphasize structural information and overlook the impact of real-world prevalent noise on feature learning and clustering with graph data, which may be detrimental to ...

To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision ...

Centre for Vision, Speech and Signal Processing (CVSSP), School of Computer Science and Electronic Engineering. Mark Plumbley is Professor of Signal Processing at the Centre for Vision, Speech and ...