The brain is a complex system comprising a myriad of interacting neurons, posing significant
challenges in understanding its structure, function, and dynamics. Network science has …

Quantum networks have experienced rapid advancements in both theoretical and
experimental domains over the last decade, making it increasingly important to understand …

Nature is a blossoming of regular structures, signature of self-organization of the underlying
microscopic interacting agents. Turing theory of pattern formation is one of the most studied …

Complex systems are characterized by multiple spatial and temporal scales. A natural
framework to capture their multiscale nature is that of multilayer networks, where different …

Higher-order networks are gaining significant scientific attention due to their ability to
encode the many-body interactions present in complex systems. However, higher-order …

Hypergraphs capture the higher-order interactions in complex systems and always admit a
factor graph representation, consisting of a bipartite network of nodes and hyperedges. As …

Hypergraphs are higher-order networks that capture the interactions between two or more
nodes. Hypergraphs can always be represented by factor graphs, ie, bipartite networks …

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models
describing oscillators on simplices rather than nodes. In this paper, we present a unified …

The dynamics of cascading failures in spatial interdependent networks significantly depends
on the interaction range of dependency couplings between layers. In particular, for an …

The stability analysis of synchronization in time-varying higher-order networked structures
(simplicial complexes) is a challenging problem due to the presence of time-varying group …