All beauty, richness and harmony in the emergent dynamics of a complex system largely
depend on the specific way in which its elementary components interact. The last twenty-five …

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 …

Gathering events, eg, going to gyms and meetings, are ubiquitous and crucial in the
spreading phenomena, which induce higher-order interactions, and thus can be described …

Networks allow us to describe a wide range of interaction phenomena that occur in complex
systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology …

Epidemiological models based on traditional networks have made important contributions to
the analysis and control of malware, disease, and rumor propagation. However, higher …

A paramount research challenge in network and complex systems science is to understand
the dissemination of diseases, information and behaviour. The COVID-19 pandemic and the …

Contagion phenomena are often the results of multibody interactions—such as
superspreading events or social reinforcement—describable as hypergraphs. We develop …

Simplicial neural networks (SNNs) have recently emerged as a new direction in graph
learning which expands the idea of convolutional architectures from node space to simplicial …

We investigate epidemic spreading in a deterministic susceptible-infected-susceptible
model on uncorrelated heterogeneous networks with higher-order interactions. We provide …

Interactions between people, including pairwise and higher-order interactions, can be
approximated as temporal higher-order networks, where the connections are constantly …