Abstract: Graph self-supervised learning (GSSL) has shown great promise in addressing the label scarcity problem in real-world graphs, yet existing generative self-supervised learning (SSL) methods ...
Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul 34349, Turkey Lab for Innovative Drugs (Lab4IND), ...
This project detects structural network anomalies using a GNN autoencoder. It contrasts this deep learning approach with the classic DBSCAN method. While DBSCAN only uses node features (CPU, RAM), the ...
ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...
This repository provides the implementation of the AEGAE method for community detection in attributed graphs. AEGAE integrates Laplacian regularization and a graph autoencoder to generate robust node ...
Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
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