IEEE

Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment

Abstract: With the rapid progress in data-driven approaches, artificial intelligence, and big data analytics technologies, utilizing electroencephalogram (EEG) signals for emotion analysis in the ...
IEEE

Dynamic Graph Convolution and Spatiotemporal Self-Attention Network for Traffic Flow Prediction

Abstract: As a typical spatiotemporal series prediction task, traffic flow prediction has found wide application in intelligent transportation systems (ITS). Despite some progress, several unresolved ...
Microsoft

xGCN An Extreme Graph Convolutional Network for Large-scale Social Link Prediction

\textit{Graph neural networks} (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and ...
GitHub

chen26sanken/Utilizing-Molecular-Descriptors-in-Graph-Convolutional-Neural-Networks

¹ SANKEN, The University of Osaka, Mihogaoka 8-1, Ibaraki, Osaka, 567-0047, JAPAN ² Graduate School of Information Science and Technology, The University of Osaka, Yamadaoka 1-5, Suita, 565-0871, ...
GitHub

DSTFGCN: A Dynamic Spatial-temporal Fusion Graph Convolution Network for Traffic Flow Forecasting

This repository contains the implementation of DSTFGCN, a deep learning model for traffic flow forecasting. Step 1: Download the dataset from Baidu Yun (Access Code: pems). Step 2: Put the four .npz ...