Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...

Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit ...

the main.py can be run from the command line interface with the following commands, where -f (dataset file) and -r (the number of parid replicates) are the two required parameters. An example command ...

Graph neural networks (GNNs) have been applied with great success across science and engineering, but we do not understand why they work so well. Motivated by ...

Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can ...

Abstract: Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are introduced into traffic prediction and achieve state-of-the-art performance due to their good ability for ...

Abstract: Urban vehicle emission prediction can help the regulation of vehicle pollution and traffic control. However, it is hard to predict the spatiotemporal variation of vehicle emission because of ...