In unsupervised graph anomaly detection, existing methods usually focus on detecting outliers by learning local context information of nodes, while often ignoring the importance of global context.

Graph anomaly detection (GAD) has attracted increasing interest due to its critical role in diverse real-world applications. Graph neural networks (GNNs) offer a promising avenue for GAD, leveraging ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Brualdi, R.A. and Goldwasser, J.L. (1984) Permanent of the Laplacian Matrix of Trees and Bipartite Graphs. Discrete Mathematics, 48, 1-21.

Welcome to LetsPS! 🎨🖥️ Master Photoshop, Illustrator, and InDesign with step-by-step tutorials designed to help you create stunning artwork! From photo manipulations and text effects to ...

We introduce an anomaly detection approach for EEG channels and segments based on inter-channel correlation analysis. This method utilizes Graph Neural Networks (GNNs) (15, 16) to capture the ...

A correlation matrix is a valuable statistical tool that appraisers can use to assess relationships between different variables, ensuring that they produce thorough, defensible, and reproducible ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.