Graph Neural Networks (GNNs) and network embedding techniques have emerged as powerful tools for analyzing and interpreting complex data structures represented as graphs. These methods are ...

Colab Notebooks covering deep learning tools for biomolecular structure prediction and design ...

Official Code for "Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction (AAAI 2021)" ...

A team of researchers has unveiled a groundbreaking method leveraging Graph Neural Networks (GNNs) and transfer entropy to significantly enhance the ...

This study introduces an innovative artificial intelligence approach based on knowledge graphs and graph neural networks. The method integrates multi-source data to construct a knowledge graph, which ...

By integrating graph neural networks with energy-based models, our approach captures intricate fault correlations and improves the accuracy of fault diagnosis. The EGN-OOD framework uses the maximal ...

A team of researchers has unveiled a groundbreaking method leveraging Graph Neural Networks (GNNs) and transfer entropy to significantly enhance the prediction of mesozooplankton community dynamics ...

Making Artificial Intelligence systems robustly perceive humans remains one of the most intricate challenges in computer ...