Compared with traditional neural networks, graph convolutional networks are very suitable for processing graph structured data. However, common graph convolutional network methods often have ...

Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such techniques explore the ...

More information: Du Lei et al, Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia, Schizophrenia Bulletin (2022).DOI: 10.1093/schbul/sbac047 ...

Keywords: COVID-19, deep learning, graph convolutional network, predicting, public transportation. Citation: Anno S, Hirakawa T, Sugita S and Yasumoto S (2022) A graph convolutional network for ...

Graph Convolutional Network-based Scheduler for Distributing Computation in the Internet of Robotic Things - ANRGUSC/gcnschedule-turtlenet. ... edGNN contains source code for solving inference ...

Keywords: EEG, driving fatigue detection, channel attention mechanism, graph convolutional network, spatial attention mechanism. Citation: Liu H, Liu Q, Cai M, Chen K, Ma L, Meng W, Zhou Z and Ai Q ...