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 …

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 …

Higher-order networks describe the many-body interactions of a large variety of complex
systems, ranging from the the brain to collaboration networks. Simplicial complexes are …

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 and simplical complexes both capture the higher-order interactions of complex
systems, ranging from higher-order collaboration networks to brain networks. One open …

These are exciting times for quantum physics as new quantum technologies are expected to
soon transform computing at an unprecedented level. Simultaneously network science is …

Many complex systems that exhibit temporal nonpairwise interactions can be represented by
means of generative higher-order network models. Here, we propose a hidden variable …

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models
describing oscillators on simplices rather than nodes. In this paper, we present a unified …

Many networks can be characterized by the presence of communities, which are groups of
units that are closely linked. Identifying these communities can be crucial for understanding …