Clustering for Graph-structured Data using Graph Neural Networks

This contribution relates to adding up a fast, yet effective graph clustering technique to PyTorch Geometric library based on Spectral Modularity Maximization.
A multi-layer graph convolutional network (GCN) is used to learn the hidden representations of each of the nodes in the graph.
Softmax activation is then applied to the output of GCN to obtain soft, yet differentiable cluster assignments being made for the input graph.
Designed and implemented unit tests to confirm the correctness of the clustering technique.
Libraries/Framework: PyTorch, PyTorch Geometric, and PyTest