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 …

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 …

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 …

In the past decade, geometric network models have received vast attention in the literature.
These models formalize the natural idea that similar vertices are likely to connect. Because …

This paper is concerned with cross-sectional dependence arising because observations are
interconnected through an observed network. Following (Doukhan and Louhichi, 1999), we …

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 …

A recent trend in the context of graph theory is to bring theoretical analyses closer to
empirical observations by focusing the studies on random graph models that are used to …

A standard approach to accelerating shortest path algorithms on networks is the
bidirectional search, which explores the graph from the start and the destination …

The hyperbolicity of a graph, informally, measures how close a graph is (metrically) to a tree.
Hence, it is intuitively similar to treewidth, but the measures are formally incomparable …