This repository provides a PyTorch implementation of the Cluster-GCN paper (KDD 2019), which uses graph clustering techniques to enable efficient SGD-based training of deep and large graph convolutional networks. It addresses the computational and memory challenges of training GCNs by sampling dense subgraphs identified through graph partitioning algorithms. The implementation includes the Amazon2M benchmark dataset with 2 million nodes and 61 million edges for scalability testing.