In this study, we investigate a Geometric Deep Learning model from a thermodynamic perspective, considering the weights as particles that are neither quantum nor relativistic. We analyze the concept of temperature, as described in a previous publication [7], and explore its behavior in different layers of GCN and GAT models. Furthermore, we discuss potential implications of our discoveries for future applications.
Analyzing Graph Neural Networks through Temperature-based Approach in Geometric Deep Learning
