Performance Analysis of GNNs Under Varying Model Depth and Hyperparameters

This project aimed to develop a solid understanding of the theory behind Graph Neural Networks (GNNs) and gain practical experience implementing them. A small-scale literature review of GNNs was first conducted in the form of a theoretical background, followed by implementing multiple models where model design and hyperparameters were varied using PyTorch Geometric. Graph Convolutional Networks (GCNs) were specifically implemented, varying the number of message passing layers, dropout rates, and optimizer choices to observe how these decisions affected performance. Model depth negatively impacted performance, likely due to over-smoothing. The other experimental results did not reveal large performance differences across different dropout rates and optimizers, but key limitations in the methodology were identified that likely influenced these outcomes. Nevertheless, the models performed very well compared to other implementations, with the best test accuracy at 81.9\%. This is close to state-of-the-art performance despite the relatively simple approach. The project highlights both the accessibility and the complexity of designing and training GNNs.