Real-world complex systems exhibit multiple levels of relationships. In many cases they
require to be modeled as interconnected multilayer networks, characterizing interactions of …

Real networks are heterogeneous structures, with edges unevenly distributed among nodes,
presenting community structure, motifs, transitivity, rich clubs, and other kinds of topological …

Random walks constitute a fundamental mechanism for a large set of dynamics taking place
on networks. In this article, we study random walks on weighted networks with an arbitrary …

In this article, we present a survey of different types of random walk models with local and
non-local transitions on undirected weighted networks. We present a general approach by …

We introduce a refined way to diffusely explore complex networks with stochastic resetting
where the resetting site is derived from node centrality measures. This approach differs from …

We introduce a formalism of fractional diffusion on networks based on a fractional Laplacian
matrix that can be constructed directly from the eigenvalues and eigenvectors of the …

Brain function depends on effective neural communication and integration across different
domains. This exchange of information is facilitated by the “connectome”: the complex …

Virtually all the emergent properties of complex systems are rooted in the non-
homogeneous nature of the behaviours of their elements and of the interactions among …

This book analyzes stochastic processes on networks and regular structures such as lattices
by employing the Markovian random walk approach. Part 1 is devoted to the study of local …

Finite Markov chains, memoryless random walks on complex networks, appear commonly
as models for stochastic dynamics in condensed matter physics, biophysics, ecology …