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Ariadne Lab · DIEEI · University of Catania

Untangling network complexity

We study how interconnected systems are organised, how they respond to disruption and how graph algorithms and machine learning can help us understand them.

Explore our research Meet the lab

Highlights

  1. A noisy random graph branches into two generated graphs: a sparse spatial graph beside a transmission tower, for infrastructure and transport networks, and a modular graph beside a howling wolf, for social networks.

    Learning on Graphs

    What a generated graph should preserve

    How can generated graphs preserve the structures that distinguish real network domains, and how can generation be steered towards a target class?

    Read more
  2. A painted brain with sampled areas joined by grey candidate links. Dashed leaders connect five areas to activity traces; the three traces that share one rhythm turn blue, and a blue sub-network appears over washed brain areas.

    Learning on Graphs

    From brain signals to meaningful networks

    How can brain activity recordings reveal the interactions that distinguish subjects and characterise brain disorders?

    Read more
  3. Illustrative food web in which a daisy wilts and its two feeding links become dashed red lines. Blue links from a healthy aster show that the bee and aphid retain another resource; other feeding links are shown in blue-gray.

    Network Robustness and Resilience

    Understanding network robustness

    How much disruption can a complex network withstand, and how can the few units whose loss fragments it be found?

    Read more Code & benchmark
  4. World wheat-trade network under a simulated US production shock. A wheat illustration wilts; dashed red arrows then show reduced flows spreading from the United States, and blue arrows show approximately conserved flows. Red country fills highlight the shock origin and selected recipients with reduced imports against a sage geographic background.

    Multilayer Networks and Mesoscale Structure

    The layered structure of global trade

    How do commodity and industry layers organise international trade, and how do production shocks travel through it?

    Read more Food Connections resources
All highlights

Research questions

We study complex networks from complementary perspectives: their organisation across scales, their response to disruption, how useful representations can be learned from graph data and how learning can support difficult graph algorithms.

All research topics

Selected publications

Manlio De Domenico, Valeria d’Andrea, Marco Grassia, Giuseppe Mangioni, Barbara Di Camillo (2026).
A digital twin should make a risky claim.
Nature Physics.
DOI
Marco Grassia, Stefano Pio Cosentino, Maria Letizia Pumo, Giuseppe Mangioni (2025).
Machine Learning Supernovae’s Progenitor Characterization.
2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP).
DOI
Luiz G. A. Alves, Giuseppe Mangioni, Francisco A. Rodrigues, Pietro Panzarasa, Yamir Moreno (2022).
The rise and fall of countries in the global value chains.
Scientific Reports.
DOI
Alessia Amelio, Giuseppe Mangioni, Andrea Tagarelli (2020).
Modularity in Multilayer Networks Using Redundancy-Based Resolution and Projection-Based Inter-Layer Coupling.
IEEE Transactions on Network Science and Engineering.
DOI
Browse all publications

Summer school

Mediterranean School of Complex Networks

An international doctoral school on complex networks. Members of the lab have co-directed it since 2022, after organising its Catania predecessor, ISI&DCN.

About the school

Work with us

We are always glad to hear from students and researchers curious about complex networks, graph algorithms and machine learning.

If you have an idea for a thesis or a collaboration, or just want to talk networks, write to Giuseppe Mangioni or Marco Grassia.

Thesis projects Meet the lab Contact details