Graph and Node Classification This repository contains Graph Neural Networks (GNNs) implementations for graph classification and node classification. It is part of Class: Deep Learning on Graphs, Fall ...

"global node" approach - for each graph, a global node is added, only global nodes are classified global mean pooling approacah - added a mean-pooling layer as the last layer (before softmax) in GCN ...

Text GCN is a model which allows us to use a graph neural network for text classification where the type of network is convolutional. The below figure is a representation of the adaptation of ...

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

Text classification aims to assign labels to textual units by making use of global information. Recent studies have applied graph neural network (GNN) to capture the global word co-occurrence in a ...

D2-GCN: Dynamic Graph Network for Node Classification Higher Education Press Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct ...

In this study, we proposed to use GCN for the classification of MS clinical forms based only on the measurement of GM morphological feature (thickness) obtained from T1w-MRI. The impacts of different ...