Network Science

CoreGDM: Geometric Deep Learning Network Decycling and Dismantling

Network dismantling deals with the removal of nodes or edges to disrupt the largest connected component of a network. In this work we introduce CoreGDM, a trainable algorithm for …

Geometric Deep Learning Graph Pruning to Speed-Up the Run-Time of Maximum Clique Enumerarion Algorithms

In this paper we propose a method to reduce the running time to solve the Maximum Clique Enumeration (MCE) problem. Specifically, given a network we employ geometric deep learning …

Warm-up: Network analysis with Python

From data to network analysis with Python

Network Topology to Predict Bibliometrics Indices: A Case Study

Link Prediction in Time Varying Social Networks

Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the …

Insights into countries’ exposure and vulnerability to food trade shocks from network-based simulations

In the context of a global food system, the dynamics associated to international food trade have become key determinants of food security. In this paper, we resort to a diffusion …

Efficient Node PageRank Improvement via Link Building using Geometric Deep Learning

Centrality is a relevant topic in the field of network research, due to its various theoretical and practical implications. In general, all centrality metrics aim at measuring the …

Correlation between researchers’ centrality and h-index: a case study

Analysis of the co-authorship sub-networks of Italian academic researchers

(Unintended) Consequences of export restrictions on medical goods during the Covid-19 pandemic

The outbreak of the Covid-19 pandemic led several governments to impose restrictions on the export of medical supplies. Despite being at odds with the canonical prescriptions of …