Network Science

Learning fine-grained search space pruning and heuristics for combinatorial optimization

Combinatorial optimization problems arise naturally in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requires considerable time, effort and experimentation. On the …

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 network dismantling via node-removal. The approach is based on …

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 in order to find a simpler network on which running the algorithm to …

Warm-up: Network analysis with Python

From data to network analysis with Python

(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 the economic theory, non-cooperative measures of this kind remain …

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 model to simulate how shocks to domestic food production propagate …

Machine learning dismantling and early-warning signals of disintegration in complex systems

From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features – e.g., heterogeneous connectivity, mesoscale organization, hierarchy – affect their robustness to …

mGNN: Generalizing the Graph Neural Networks to the Multilayer Case

Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new way to approach many real-world problems that would be hardly or even …

wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction

Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose wsGAT, an extension of the Graph Attention Network (GAT) layers, meant to …

Group Cohesion Assessment in Networks

Networks measurement is essential to catch and quantify their features, behaviour and/or emerging phenomena. The goal of cohesiveness metric introduced here is to establish the level of cohesion among network nodes. It comes from the Black-Hole …