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 …

Complex networks have become the main paradigm for modelling the dynamics of
interacting systems. However, networks are intrinsically limited to describing pairwise …

Higher-order networks have emerged as a powerful framework to model complex systems
and their collective behavior. Going beyond pairwise interactions, they encode structured …

Time series analysis has proven to be a powerful method to characterize several
phenomena in biology, neuroscience and economics, and to understand some of their …

A deluge of new data on real-world networks suggests that interactions among system units
are not limited to pairs, but often involve a higher number of nodes. To properly encode …

Hypergraphs, encoding structured interactions among any number of system units, have
recently proven a successful tool to describe many real-world biological and social …

Hypergraphs, describing networks where interactions take place among any number of
units, are a natural tool to model many real-world social and biological systems. Here, we …

From social to biological systems, many real-world systems are characterized by higher-
order, non-dyadic interactions. Such systems are conveniently described by hypergraphs …

Representing social systems as networks, starting from the interactions between individuals,
sheds light on the mechanisms governing their dynamics. However, networks encode only …

Higher-order interactions play a key role for the operation and function of a complex system.
However, how to identify them is still an open problem. Here, we propose a method to fully …