Check out the R package diffudist, now also on CRAN! The diffudist package allows you to easily evaluate (and plot with ggplot2) diffusion distances between the nodes of a complex networks and, in so doing, analaysing the functional shape and diffusion geometry of your networks.

Book of Abstracts of the Conference on Complex Systems 2020

In 2017 Manlio De Domenico introduced the family of diffusion distances and induced geometry as a tool for the analysis of the functional organisation of complex networks. Its natural generalisation to multilayer networks was still missing and with this work we filled the gap providing (i) a rigorous mathematical definition of the diffusion distance(s) and induced space(s) in the framework of multilayer networks, (ii) the extension of the diffusion distance definition w.r.t. different random walk dynamics, and (iii) a detailed analysis of the interplay between layer topology, inter-layer connectivity, layer-layer correlations (in terms of edge/partition overlapping), and random walk dynamics.

We introduce in this study CovMulNet19, a comprehensive COVID-19 network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them.

Exchanging information is crucial for many real systems and consequently also assessing the how efficiently a system carries on this task. Here we assume that the pairwise communication efficiency is inversely proportional to their distance (metric on the network). Furthermore, we focus on the efficiency that can be quantified through the topology of and the flows on the network.

Centrality descriptors are widely used to rank nodes according to specific concept(s) of importance. Despite the large number of centrality measures available nowadays, it is still poorly understood how to identify the node which can be considered as …

Increasing evidence suggests that cities are complex systems, with structural and dynamical features responsible for a broad spectrum of emerging phenomena. Here we use a unique data set of human flows and couple it with information on the underlying …

This is a small demo related to our (Giulia Bertagnolli, Claudio Agostinelli, Manlio De Domenico) recent work, Network depth: identifying median and contours in complex networks, Journal of Complex Networks 8 (4).

A series of three seminars on complex networks for master students in Mathematics (not only) at the University of Trento.
Course material is available in my github and you can interact with the Jupyter notebooks here

Abstract A statistical data depth $d(x, \mathbb{P})$ is a measure of depth or outlyingness of a sample $x \in \mathbb{R}^p$ with respect to its underlying distribution $\mathbb{P}$ and it provides a centre-outward ordering of sample points.