Tech Xplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
IEEE

Dynamic Graph Neural Network with Motif Reconstruction

Abstract: Dynamic graph representation learning aims to generate low-dimensional latent vector representations of graphs or nodes at various time points from evolving graph datas, which are then used ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Frontiers

DGAT: a dynamic graph attention neural network framework for EEG emotion recognition

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...
C&EN

The Dynamic Connection Layer (DCL): Enhancing Topological Representation in Chemical Graph Neural Networks

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. In ...