Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

Spreading processes have been largely studied in the literature, both analytically and by
means of large-scale numerical simulations. These processes mainly include the …

The whole frame of interconnections in complex networks hinges on a specific set of
structural nodes, much smaller than the total size, which, if activated, would cause the …

Spectral algorithms are classic approaches to clustering and community detection in
networks. However, for sparse networks the standard versions of these algorithms are …

But, perhaps, we should start with a few words about graphs in general. They are, of course,
one of the prime objects of study in Discrete Mathematics. However, graphs are among the …

A non-backtracking walk on a graph is a directed path such that no edge is the inverse of its
preceding edge. The non-backtracking matrix of a graph is indexed by its directed edges …

Graph theory meets number theory in this stimulating book. Ihara zeta functions of finite
graphs are reciprocals of polynomials, sometimes in several variables. Analogies abound …

By leveraging large clusters of commodity hardware, the Cloud offers great opportunities to
optimize the operative costs of software systems, but impacts significantly on the reliability of …

It was conjectured by Alon and proved by Friedman that a random $ d $-regular graph has
nearly the largest possible spectral gap, more precisely, the largest absolute value of the …

Community detection is a fundamental problem in network analysis with many methods
available to estimate communities. Most of these methods assume that the number of …