Networks are finite metric spaces, with distances defined by the shortest paths between
nodes. However, this is not the only form of network geometry: two others are the geometry …

Random graph (RG) models play a central role in complex networks analysis. They help us
to understand, control, and predict phenomena occurring, for instance, in social networks …

Complex networks are key to describing the connected nature of the society that we live in.
This book, the second of two volumes, describes the local structure of random graph models …

Real-world networks, like social networks or the internet infrastructure, have structural
properties such as large clustering coefficients that can best be described in terms of an …

Hyperbolic geometry appears to be intrinsic in many large real networks. We construct and
implement a new maximum likelihood estimation algorithm that embeds scale-free graphs in …

In this paper we consider the clustering coefficient, and clustering function in a random
graph model proposed by Krioukov et al. in 2010. In this model, nodes are chosen randomly …

Turing patterns, arising from the interplay between competing species of diffusive particles,
have long been an important concept for describing nonequilibrium self-organization in …

One of the pillars of the geometric approach to networks has been the development of model-
based mapping tools that embed real networks in its latent geometry. In particular, the tool …

Clustering–the tendency for neighbors of nodes to be connected–quantifies the coupling of
a complex network to its latent metric space. In random geometric graphs, clustering …

Real networks often grow through the sequential addition of new nodes that connect to older
ones in the graph. However, many real systems evolve through the branching of …