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 …

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of
the graph. These methods are attractive because they enable targeted clustering around a …

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 …

Recent studies have shown that novel collective behaviors emerge in complex systems due
to the presence of higher-order interactions. However, how the collective behavior of a …

Phenotype robustness to environmental fluctuations is a common biological phenomenon.
Although most phenotypes involve multiple proteins that interact with each other, the basic …

Integrated Information Theory (IIT) is a prominent theory of consciousness that has at its
centre measures that quantify the extent to which a system generates more information than …

Many empirical networks are intrinsically polyadic, with interactions occurring within groups
of agents of arbitrary size. There are, however, few flexible null models that can support …

Network data has become widespread, larger, and more complex over the years. Traditional
network data is dyadic, capturing the relations among pairs of entities. With the need to …

The connectivity of a network contains information about the relationships between nodes,
which can denote interactions, associations, or dependencies. We show that this information …

With the increasing application of network science, understanding the behavior of higher-
order networks has become particularly important. Especially, the study of multilayer higher …