Node classification in the heterophilic regime via diffusion-jump GNNs.

In the ideal (homophilic) regime of vanilla GNNs, nodes belonging to the same community have the same label: most of the nodes are harmonic (their unknown labels result from averaging those of their neighbors given some labeled nodes). In other words, heterophily (when neighboring nodes have different labels) can be seen as a "loss of harmonicity". In this paper, we define "structural heterophily" in terms of the ratio between the harmonicity of the network (Laplacian Dirichlet energy) and the harmonicity of its homophilic version (the so-called "ground" energy).

Predicting COVID-19 pandemic waves including vaccination data with deep learning.

Introduction: During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear.

Methods: We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines.