In one of the variants of Graph-based recommenders, GCMC i.e.; graph convolutional matrix completion, the user-item interaction is converted into a bipartite graph. The edges of the graph contain information like ratings. One main component of the model is an autoencoder that encodes this edge information in such a way that it can be reconstructed from this encoded or compressed representation. This is done by passing the bipartite graph through several message passing layers to learn the representation of each user and item. Finally, link prediction is performed with these embeddings.