Many graph and node-level problems are fundamentally about diffusion. Information diffuses from one node to another. In this way, solutions to these problems can be modeled after heat kernel, which satisfies heat (differential) equation, which is about diffusion of heat in surrounding environment. Heat kernel is also used to analyze various properties of random walks on graphs.
With this in mind, a Neural ODE model on graphs can result in better performance for our problem than GNN + RNN/Transformer.
Many graph and node-level problems are fundamentally about diffusion. Information diffuses from one node to another. In this way, solutions to these problems can be modeled after heat kernel, which satisfies heat (differential) equation, which is about diffusion of heat in surrounding environment. Heat kernel is also used to analyze various properties of random walks on graphs. With this in mind, a Neural ODE model on graphs can result in better performance for our problem than GNN + RNN/Transformer.