Network science has evolved into an indispensable platform for studying complex systems.
But recent research has identified limits of classical networks, where links connect pairs of …

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 …

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 …

Networks provide a powerful formalism for modeling complex systems by using a model of
pairwise interactions. But much of the structure within these systems involves interactions …

Using graphs to model pairwise relationships between entities is a ubiquitous framework for
studying complex systems and data. Simplicial complexes extend this dyadic model of …

The goal of this paper is to establish the fundamental tools to analyze signals defined over a
topological space, ie a set of points along with a set of neighborhood relations. This setup …

The emergence of synchronization in systems of coupled agents is a pivotal phenomenon in
physics, biology, computer science, and neuroscience. Traditionally, interaction systems …

Many complex systems find a convenient representation in terms of networks: structures
made by pairwise interactions (links) of elements (nodes). For many biological and social …

We investigate consensus dynamics on temporal hypergraphs that encode network systems
with time-dependent, multiway interactions. We compare these consensus processes with …

The study of dynamical processes in networked systems is one of the central problems in
complexity science. 1, 2 Even simple dynamical systems, when connected with each other …