The betweenness centrality (BC) is an important quantity for understanding the structure of
complex large networks. However, its calculation is in general difficult and known in simple …

Among the many features of natural and man-made complex networks the small-world
phenomenon is a relevant and popular one. But, how small is a small-world network and …

Many machine learning algorithms used for dimensional reduction and manifold learning
leverage on the computation of the nearest neighbors to each point of a data set to perform …

Studies on road networks, especially on highly-urban areas, have to account not only for the
topological (ie network structure) but, more so, for the actual physical and geographical …

Urban road networks (URNs), as simplified views and important components of cities, have
different structures, resulting in varying levels of transport efficiency, accessibility, resilience …

In this perspective, we introduce recent research into the structure and function of complex
investor networks supporting sustainability efforts. Using the case of solar, wind and hydro …

Insight into the blockage vulnerability of evolving spatial networks is important for
understanding transport resilience, robustness, and failure of a broad class of real-world …

In the present paper, we study the robustness of two-dimensional random lattices (Delaunay
triangulations) under attacks based on betweenness centrality. Together with the standard …

A 1d random geometric graph (1d RGG) is built by joining a random sample of $ n $ points
from an interval of the real line with probability $ p $. We count the number of $ k $-hop …

We consider the statistical properties of arrival times and balls on first-passage percolation
(FPP) two-dimensional square lattices with strong disorder in the link times. A previous work …