Understanding communication and information processing in nervous systems is a central
goal of neuroscience. Over the past two decades, advances in connectomics and network …

The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Most graph neural networks (GNNs) use the message passing paradigm, in which node
features are propagated on the input graph. Recent works pointed to the distortion of …

The constituents of many complex systems are characterized by non-trivial connectivity
patterns and dynamical processes that are well captured by network models. However, most …

Complex networks are ubiquitous: a cell, the human brain, a group of people and the
Internet are all examples of interconnected many-body systems characterized by …

We propose a novel class of graph neural networks based on the discretized Beltrami flow, a
non-Euclidean diffusion PDE. In our model, node features are supplemented with positional …

Complex systems must respond to external perturbations and, at the same time, internally
distribute information to coordinate their components. Although networked backbones help …

The renormalization group is the cornerstone of the modern theory of universality and phase
transitions and it is a powerful tool to scrutinize symmetries and organizational scales in …

When a large number of similar entities interact among each other and with their
environment at a low scale, unexpected outcomes at higher spatio-temporal scales might …

This paper focuses on spectral graph convolutional neural networks (ConvNets), where
filters are defined as elementwise multiplication in the frequency domain of a graph. In …