VII Mediterranean School of Complex Networks

Catania, Sicily 25 June - 02 July 2022


Due to a logistic problem, preventing us from guaranteeing safety measures against COVID19, we have moved the school location to Catania. All the attendants have been promptly informed. This message is just to specify that most of the information available in our website is related to Salina, not Catania. The School will take place within the 4-stars Hotel Nettuno (
Further information about Catania is available here:

Due to this change, it is no more needed that you travel with the School bus/hydrofoil, which have been cancelled. You can arrive in Catania at your best convenience on 25th June 2022 and reach the Hotel by yourself via taxi or public transportation (bus). Similarly, you might depart from Catania at any time on 2nd July 2022, since the School will officially finish on 1st July 2022.
We will share with you a vademecum with all the relevant information by the end of May.

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Arrival at the hotel, self-organized by the attendant.

9:00 - 10:00    OPENING

Giuseppe Mangioni (University of Catania, Italy)

10:00 - 10:30    Individual Presentations

10:30 - 11:00    COFFEE BREAK

11:00 - 12:30    project group and challenge

16:00 - 17:00    WARM UP I

Valentina Gambuzza (Università degli studi di Catania, Italy)

Brief introduction to complex networks
The aim of this tutorial is to briefly recall some features of complex networks. We will start from basic definitions useful to understand the structure of a network and how they characterize the different models. We will go through the most important models used to build up networks with different features and finally we will discuss some applications of complex networks.

17:00 - 17:30    COFFEE BREAK

17:30 - 18:30    WARM UP II

Marco Grassia (Università degli studi di Catania, Italy)

Python is the most widely used programming language in data analysis. In this warmup, we will introduce the most popular and powerful libraries for Network Science (i.e., graph-tool, NetworkX), for data analysis and visualization (Matplotlib, NumPy, SciPy, Pandas, Seaborn), and for Deep Learning on graphs (PyTorch Geometric).

SESSION I: Discovering governing equations: from network structure to dynamics

Roger Guimerà (URV, Spain)

From network inference to the discovery of equations from data
Network inference is the process of extracting information from network data; some typical problems in network inference include link prediction or the discovery of network communities. Although there exist heuristic methods to solve these problems, methods based on the formulation of generative models and the rigorous use of probability theory are often preferable. In the first part of this lecture we will describe these approaches to network inference, starting from general concepts of Bayesian model selection. In the second part of the lecture, we will discuss how we can use similar approaches to a seemingly unrelated problem, namely the problem of learning closed-form mathematical models from data.

9:00 - 10:30    PART I

10:30 - 11:00    COFFEE BREAK

11:00 - 12:30    PART II

16:00 - 17:00    STUDENT TALKS

17:00 - 17:30    COFFEE BREAK

17:30 - 18:30    STUDENT TALKS

SESSION II: Network Neuroscience

Fabrizio De Vico Fallani (INRIA)

Network science for understanding brain complexity
In the last decades, network science has become essential for studying complex interconnected systems. Combined with neuroimaging, network science has allowed to visualize brain connectivity patterns and quantify their key organizational properties. Within this expanding multidisciplinary field many issues remain open, from how to filter connectivity information to how to model temporally dynamic brain networks and integrate information from multimodal connectivity. In this presentation, I will focus on these challenges and discuss the potential impact through a selection of results obtained in human neuroscience.

9:00 - 10:30    PART I

10:30 - 11:00    COFFEE BREAK

11:00 - 12:30    PART II

16:00 - 17:00    STUDENT TALKS

17:00 - 17:30    COFFEE BREAK

17:30 - 18:30    STUDENT TALKS

SESSION III: Spreading Processes on Networks

Jesus Gomez-Gardeñz (Universidad de Zaragoza, Spain)

In this lecture we will address a topic that has advanced enormously in recent decades thanks to the contribution of network science: the modeling of the impact that social behavior has on the onset of an epidemic or on the spread of information. We will begin by reviewing the building blocks of spreading processes, compartmental models, and the derivation of the basic reproductive number. From there, we will progressively add ingredients aimed at capturing the actual patterns of connectivity (networks) and mobility (metapopulations) observed in our society. Finally, after analyzing the behavior of these models from a theoretical point of view, we will address their application in theoretical epidemiology and the design of non-pharmacological containment strategies, i.e. those that act by changing our social behavior or taking advantage of it to mitigate or suppress pathogen transmission.

9:00 - 10:30    PART I

10:30 - 11:00    COFFEE BREAK

11:00 - 12:30    PART II

15:00 - 22:00    Etna Volcano Tour

9.00 - 10.30    Focused Seminars I

Community detection
Clara Granell (Universitat Rovira i Virgili, Spain)
Community detection is an important problem that consists on grasping the intrinsic topological structures of networked data, without any previous knowledge about the size or number of groups to be found. This is of utmost importance in exploratory data analysis, specially in experimental fields like biology, chemistry, and many others. The main difficulty that scientists face when trying to do community analysis relies on finding the appropriate definitions and algorithms for each problem at hand. Nowadays, a myriad of methods are available, and some are even embedded in network analysis tools, making it easy for scientists to apply the most popular community algorithm right away, but also hiding the whole community detection process in a black box. In this lecture we will review community detection from its very definition, considering the advantages and drawbacks of the most popular approaches, in hopes to build a grounded knowledge about this problem so that every scientist is able to critically choose the appropriate solution for his problem.

10.30 - 11.00    COFFEE BREAK

11.00 - 12:30    Focused Seminars II

Andrea Baronchelli (City University of London and The Alan Turing Institute, UK)
The scientific paper “Bitcoin: A Peer-to-Peer Electronic Cash System” by Satoshi Nakamoto, published in 2008, started a socio-economic revolution which is still unfolding under our eyes. Today, cryptocurrencies alone have a market capitalisation of 1.7 billion Euros, the governments of 60+ countries are experimenting with central bank digital currencies and the NFT are deeply transforming such diverse sectors as the art market, the gaming industry, the fashion industry, the music business and even the real estate market. In this talk, after introducing key concepts upon which the blockchain ecosystem is unfolding, we will discuss some recent results that help us make sense of the self-organisation taking place at all levels of this revolution, including the development, usage, market and governance of cryptocurrencies and NFTs.

16.00 - 17.00    Projects Time

17.00 - 17.30    COFFEE BREAK

17.30 - 18:30    Projects Time

20.00 - 22:00    social dinner

9.00 - 10.30    Focused Seminars III

Statistically validated networks in Finance
Rosario Mantegna (University of Palermo, Italy)
We present an overview of statistically validated networks, i.e., event or relationship networks where a subset of links and edges are selected according to a statistical test of a null hypothesis. We present two case studies. In the first case study [1], we investigate daily trading decisions of individual investors. We construct the time-evolution of statistically validated networks of investors, and we obtain clusters of investors—and their time evolution— which are characterized by similar trading profiles. Our empirical observations show the presence of an ecology of groups of investors characterized by different attributes and by various investment styles over many years. In the second case study [2], we consider the trading networks occurring in a venue of a financial market with a state-of-the-art technological infrastructure. These studies detect a sizable increase in both the number and persistence of networked relationships occurring between market members in most recent years and show how technological and regulatory innovations affect the networked nature of markets.

[1] Musciotto, F., Marotta, L., Piilo, J. and Mantegna, R.N., 2018. Long-term ecology of investors in a financial market. Palgrave Communications, 4(1), pp.1-12.
[2] Musciotto, F., Piilo, J. and Mantegna, R.N., 2021. High-frequency trading and networked markets. Proceedings of the National Academy of Sciences, 118(26).

10.30 - 11:00    COFFEE BREAK

11:00 - 12:30    Focused Seminars IV

Data for good
Kyriaki Kalimeri (ISI Turin, Italy)
Every field has data. We use data everyday to extract knowledge, interpret the world around us, and make decisions. “Data for Good” is an emerging discipline where data is used to address societal challenges, bringing humanistic perspectives as—not after—new science and technology are invented.
The research questions in this area are often more complex than those proposed in computer science since cultural, moral, and other human factors are entangled within the phenomena under investigation. The data employed to answer those questions entail the same complexity with sparsity and data biases issues to require the combination of data originating from a wide range of sources to depict a more concrete view of the problem.
Finally, the unambiguous measures of validity, to which we are accustomed to in computer science, are rare, and when present, they are only partial evidence in support of a broader argument. Careful observation, critical abilities, and collaboration with practitioners with in-depth field knowledge are crucial elements for successfully introducing AI in addressing societal challenges.

In this seminar, we will cover the following topics:

    - What, Why, & How. Defining the Research Question
    - Data Sources and Biases
    - Machine learning, Performance Metrics, and Algorithmic Fairness
    - Opportunities, Challenges, Limitations
    - Ethics


17.00 - 17.30    COFFEE BREAK


Manlio De Domenico (University of Padua, Italy)


Lecturers Speaker School Directors
1 Fabrizio De Vico Fallani (INRIA, France) Andrea Baronchelli (University of London, UK) Alex Arenas (Universitat Rovira i Virgili, Spain)
2 Jesus Gomez-Gardeñz (Universida de Zaragoza, Spain) Clara Granell (Universitat Rovira i Virgili, Spain) Vincenza Carchiolo   (University of Catania, Italy)
3 Roger Guimerà   (URV, Spain) Kyriaki Kalimeri (ISI Turin, Italy) Manlio De Domenico (University of Padua, Italy)
4 Rosario Mantegna (University of Palermo, Italy) Mattia Frasca   (University of Catania, Italy)
5 Giuseppe Mangioni   (University of Catania, Italy)
6 Local organizing
7 Agnello Serafina
8 Riccardo Gallotti (FBK, Italy)




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