Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters

Understand

High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure.

• However, most of the existing HO-GNNs are hop-based, i.e., they rely on the powers of the transition matrix.
• As a result, these architectures are not fully reactive to the classification loss and the achieved structural filters have static supports.
• In other words, neither the filters' supports nor their coefficients can be learned with these networks.