Model-agnostic Graph Explainability

This contribution aims at adding a Graph Explainability solution powered by hard-concrete distribution to PyTorch Geometric. based on Spectral Modularity Maximization.
Implemented a post-hoc model-agnostic graph explainability technique, which, at every hidden layer of the GNN model, computed explanation weights for
every edge in the population and produced explanatory subgraph as output.
Softmax activation is then applied to the output of GCN to obtain soft, yet
differentiable cluster assignments being made for the input graph.
Implemented custom unit tests to ensure proper execution of the Graph
Explainability technique.
Libraries/Framework: Numpy, tqdm, PyTorch, PyTorch Geometric, and PyTest