  This paper analyses LightGCN in the context of graph recommendation
algorithms. Despite the initial design of Graph Convolutional Networks for
graph classification, the non-linear operations are not always essential.
LightGCN enables linear propagation of embeddings, enhancing performance. We
reproduce the original findings, assess LightGCN's robustness on diverse
datasets and metrics, and explore Graph Diffusion as an augmentation of signal
propagation in LightGCN.
