Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Abstract Graph Neural Networks (GNNs) are limited in their expressive power, struggle with
long-range interactions and lack a principled way to model higher-order structures. These …

The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …

In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …

Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …

Many dynamical effects in biology, social and technological complex systems have recently
revealed their relevance to group interactions beyond traditional dyadic relationships …

We consider the construction of neural network architectures for data on simplicial
complexes. In studying maps on the chain complex of a simplicial complex, we define three …

The natural world is full of complex systems characterized by intricate relations between
their components: from social interactions between individuals in a social network to …

Topological signals, ie, dynamical variables defined on nodes, links, triangles, etc. of higher-
order networks, are attracting increasing attention. However, the investigation of their …