| dc.description.abstract | Every single person, animal, or thing we can see in the world around us
is part of a broader collection of components that can spontaneously self-
organize to exhibit non-trivial global structures and behaviors at larger
scales, often without external intervention, central authorities, or lead-
ers. The properties of the collection these components give life cannot be
understood or predicted from the full knowledge of its elements alone.
Each collection is an example of complex systems whose behavior is in-
trinsically challenging to model due to the high non-linearity of the inter-
actions between its constituents.
Traditionally, complex systems have been successfully studied through
graphs abstracting the underlying network with vertices and edges con-
necting pairs of interacting components. Over the years, the scientifc
community has enriched the graph modeling framework for better cap-
turing the richness of the interactions among such units. However, graphs
have a substantial limitation encoded in their nature: they exclusively cap-
ture pairwise interactions. Yet, many complex systems are characterized
by group interactions that cannot be described simply in terms of dyads.
Studying such systems hence require new mathematical frameworks and
scientifc methodologies for its investigation.
Hypergraphs are the perfect candidates to tackle this task. A hyper-
graph is a generalization of a graph, where a (hyper)edge allows the con-
nection of an arbitrary number of vertices. However, the powerful ex-
pressiveness of hypergraphs has a few drawbacks: dealing with the com-
plexity of such data structures and the lack of appropriate tools and al-
gorithms for their study. For this reason, hypergraphs have been little
used in literature in favor of their graph-counterpart. Recently, this trend
has been drifting, thanks to an increasing number of systematic stud-
ies demonstrating that considering the higher-order structure of complex
systems can enhance our modeling capacities and help us understand and
predict their dynamical behavior. [...] [edited by Author] | it_IT |
