Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

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 …

Learning representations of sets of nodes in a graph is crucial for applications ranging from
node-role discovery to link prediction and molecule classification. Graph Neural Networks …

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 …

Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

Temporal networks serve as abstractions of many real-world dynamic systems. These
networks typically evolve according to certain laws, such as the law of triadic closure, which …

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

Scientific editors shape the content of academic journals and set standards for their fields.
Yet, the degree to which the gender makeup of editors reflects that of scientists, and the rate …

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 …