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 …

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 …

Complex networks have been successfully used to describe the spread of diseases in
populations of interacting individuals. Conversely, pairwise interactions are often not …

Topological Data Analysis (TDA) is a recent and fast growing field providing a set of new
topological and geometric tools to infer relevant features for possibly complex data. This …

We introduce a method for training neural networks to perform image or volume
segmentation in which prior knowledge about the topology of the segmented object can be …

Network neuroscience has relied on a node-centric network model in which cells,
populations and regions are linked to one another via anatomical or functional connections …

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 …

Various systems in physics, biology, social sciences and engineering have been
successfully modeled as networks of coupled dynamical systems, where the links describe …

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

Network models describe the brain as sets of nodes and edges that represent its distributed
organization. So far, most discoveries in network neuroscience have prioritized insights that …