Then, the graph and node information [biosignal information, including the joint kinematics and surface electromyography (sEMG)] are used as the inputs to the GCN for diagnosis and classification of ...

Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. The selection of activation functions and ...

Research team led by Chuliang Weng introduces D2-GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node representation ...

Recently, many researchers have applied Graph Convolutional Neural Networks (GCN) to multi-label learning tasks by establishing relations among labels. However, these researchers have only used GCN as ...

Reproduce a standard GCN (Graph Convolutional Network) using a benchmark graph classification dataset (e.g., MUTAG). Adapt the model to work with tree-structured graphs by creating a custom dataset ...

Survey on Using GCN for Semi-supervised graph node classification This is a PyTorch implementation for the paper Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017).