This repository contains the implementation of a two-part graph learning task: Reproduce a standard GCN (Graph Convolutional Network) using a benchmark graph classification dataset (e.g., MUTAG).

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

This is a PyTorch implementation for the paper Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017). Training This will train a two layer gcn on Cora Dataset for node ...

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 ...

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 ...

The C-GCN method combines the coherence network with GCN, retains the advantages of the two networks, and provides a guarantee for the classification and recognition of MI tasks in SCI patients.

For disordered images or intricate structures, the GCN has difficulties to identify all features. They proposed a novel neural network architecture–Deep Graph Convolutional Neural Network to extract ...