These are exciting times for quantum physics as new quantum technologies are expected to
soon transform computing at an unprecedented level. Simultaneously network science is …

Graph theory is now becoming a standard tool in system-level neuroscience. However,
endowing observed brain anatomy and dynamics with a complex network structure does not …

The renormalization group is the cornerstone of the modern theory of universality and phase
transitions and it is a powerful tool to scrutinize symmetries and organizational scales in …

Hypergraphs and simplical complexes both capture the higher-order interactions of complex
systems, ranging from higher-order collaboration networks to brain networks. One open …

The network density matrix formalism allows for describing the dynamics of information on
top of complex structures and it has been successfully used to analyze, eg, a system's …

The geometric renormalization technique for complex networks has successfully revealed
the multiscale self-similarity of real network topologies and can be applied to generate …

Physical networks are made of nodes and links that are physical objects embedded in a
geometric space. Understanding how the mutual volume exclusion between these elements …

A compact and tractable two-dimensional model to generate the topological network
structure of domain walls in BiFeO3 thin films is presented in this study. Our method …

Oscillatory complex networks in the metastable regime have been used to study the
emergence of integrated and segregated activity in the brain, which are hypothesised to be …

Modern theories of phase transitions and scale invariance are rooted in path integral
formulation and renormalization groups (RGs). Despite the applicability of these approaches …