GitHub

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from ...
GitHub

vera-codes6/gcn-pytorch-training

A comprehensive and modular PyTorch template for training Graph Neural Networks (GNNs) on node classification tasks. This repository provides a clean, extensible framework for experimenting with ...
marktechpost

This AI Paper Introduces SuperGCN: A Scalable and Efficient Framework for CPU-Powered GCN Training on Large Graphs

Graph Convolutional Networks (GCNs) have become integral in analyzing complex graph-structured data. These networks capture the relationships between nodes and their attributes, making them ...
IEEE

L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks

Abstract: Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets. They need to compute node representations ...