The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

In the past 15 years, statistical physics has been successful as a framework for modelling
complex networks. On the theoretical side, this approach has unveiled a variety of physical …

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 …

We bring rigor to the vibrant activity of detecting power laws in empirical degree distributions
in real-world networks. We first provide a rigorous definition of power-law distributions …

The observed architecture of ecological and socio-economic networks differssignificantly
from that of random networks. From a network science standpoint, non-random structural …

Random graph null models have found widespread application in diverse research
communities analyzing network datasets, including social, information, and economic …

Many physical and social systems are best described by networks. And the structural
properties of these networks often critically determine the properties and function of the …

Many real-world networks display a community structure. We study two random graph
models that create a network with similar community structure as a given network. One …

Real network data is often incomplete and noisy, where link prediction algorithms and
spurious link identification algorithms can be applied. Thus far, it lacks a general method to …

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging
problem of practical importance in many fields of science. Current methods of network …