Geometric Deep Learning: a Temperature Based Analysis of Graph Neural
  Networks
M. Lapenna F. Faglioni F. Zanchetta R. Fioresi
We examine a Geometric Deep Learning model as a thermodynamic system treating
the weights as non-quantum and non-relativistic particles. We employ the notion
of temperature previously defined in [7] and study it in the various layers for
GCN and GAT models. Potential future applications of our findings are
discussed.