15:00    Welcome

Meeting point at Catania airport with the School bus.

10:00 - 11:00    WARMUP

11:00 - 12:00    Individual Presentations

12:00 - 13:00    Projects


10:00 - 11:00    PART I

11:00 - 11:30    COFFEE BREAK

11:30 - 13:00    PART II

Vito Latora (Queen Mary University of London)
The constituents of a wide variety of real-world complex systems interact with each other in complicated patterns that can encompass multiple types of relationships and change in time. Recently, the interest of the research community towards such systems has increased because accounting for their "multilayer" features is a challenge. In this lecture, we will review the most recent advances in this new field, with main attention to the emergent properties induced by the structure of multiplex networks.
Topics covered:
- From complex systems to multilayer networks
- Structural properties of multilayer networks
- Reducibility of multilayer networks
- Dynamical properties of multulayer networks

17:00 - 18:00    STUDENT TALKS

18:00 - 18:30    COFFEE BREAK


Valerio Maggio (FBK, Italy)

Python is nowadays considered one of "the" programming languages for data science, thanks to its very shallow learning curve, and a solid stack of libraries and modules for efficient numerical processing and data visualisation. For what concerns _Network Analysis_ in particular, the 'networkx' package provides a comprehensive set of pre-defined tools to process and manipulate network data in a very easy and intuitive way.
This lecture will introduce the basics and practical aspects of graph theory using Python and 'networkx'. Case study will be also discussed and analysed.
The lecture is designed as a live tutorial presentation, in which all the materials and lecture notes will be available in the form of interactive Jupyter notebooks, for further study and use.
The data used, and the code presented throughout the tutorial will be also made available for reference afterwards.

Lecture notes and instructions to setup a working Python environment, are available on GitHub at:


Basic programming skills are assumed, and required to attend the lecture.


It is highly recommended to bring your laptop with you to get the best out of the tutorial, and to have fun with exercises and challenges. If you don't have any (or simply did prefer not to bring it), no worries! You can join other colleagues and work as a team :P

Session II: Higher-Order Network Models.

10:00 - 11:00    PART I

11:00 - 11:30    COFFEE BREAK

11:30 - 13:00    PART II

Martin Rosvall (Umea University, Sweden)

Higher-order network models.

Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking and spreading analysis, although it ignores a potentially important feature of the dynamics: where flows move to may depend on where they come from. In this lecture I will analyze pathways from different systems and show that ignoring the effects of higher-order Markov dynamics has important consequences for community detection, ranking, and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. Consequently, accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.

17:00 - 18:00    STUDENT TALKS

18:00 - 18:30    COFFEE BREAK


Session III: Critical Phenomena in Complex Networks.

10:00 - 11:00    PART I

11:00 - 11:30    COFFEE BREAK

11:30 - 13:00    PART II

Dirk Brockmann ( Humboldt University of Berlin, Germany )

16:00 - 20:00    Social Boat

Session IV: Spreading Processes in Complex Networks.

10:00 - 11:00    PART I

11:00 - 11:30    COFFEE BREAK

11:30 - 13:00    PART II

Jesus Gomez-GardeƱez ( Universidad de Zaragoza, Spain )

Spreading processes: From Networks to Metapopulations

In this lecture we will combine two fields: theoretical epidemiology and mobility datasets. This will allow us to construct theoretical models capturing the back-and-forth movements (such as daily commutes) and the elementary contagion processes at work. We will introduce these framework from the basic compartmental models to the more elaborated metapopulation ones, showing the importance of characterizing urban and regional mobility patterns to understand the spread of an epidemic. Finally, we will particularize on the study of vector-borne diseases (such as Dengue, Chingunya and Zika) in urban systems, showing the reliability of these kind of approaches.


1) Compartmental models
2) Networks and epidemics
3) Metapopulation approaches & recurrent mobility patterns
5) Mobility detriments spreading
6) Applications to Vector-borne diseases

16.30-17.30    Focused Seminars I

Advances in Network Neuroscience
Joaquin Goñi (Purdue University, Indiana)

On the quest of fingerprints in brain networks: identifiability and beyond
In the 17th century, physician Marcello Malpighi observed the existence of patterns of ridges and sweat glands on fingertips. This was a major breakthrough and originated a long and continuing quest for ways to uniquely identify individuals based on fingerprints. In the modern era, the concept of fingerprinting has expanded to other sources of data, such us voice recognition and retinal scans. It is only in the last few years that technologies and methodologies have achieved high-quality data for individual human brain imaging, and the subsequent estimation of structural and functional connectivity. In this context, the next challenge for human identifiability is posed on brain data, particularly on brain networks, both structural and functional.
I will present how the individual fingerprint of a connectome (as represented by a network) can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of brain connectivity modes. By using data from the Human Connectome Project, I will introduce different extensions of this work, including subject identifiability, heritability analysis of brain networks, as well as identifiability when assessing inter-task brain functional networks. Finally, results on this framework for inter-scan identifiability based on a second dataset acquired at Purdue University will be also discussed.

18:00 - 18:30    COFFEE BREAK

18:30 - 20:00    Focused Seminars I

Cognitive Network Analysis
Andrea Baronchelli ( City University of London, UK )

The Dynamics of Social Conventions: From Names to Cryptocurrencies.
How do conventions emerge and evolve in complex decentralized social systems? This question engages fields as diverse as sociology, linguistics, cognitive science and network science. Various attempts to solve this puzzle pre-suppose that formal or informal institutions are needed to facilitate a solution. The complex systems approach, by contrast, hypotheses that such institutions are not necessary in order for social consensus to form. In this talk, I will present experimental results that demonstrate the spontaneous creation of universally adopted social conventions. In doing so, I will show how a population's network structure controls the dynamics of norm formation, as captured by the simple Naming Game model. Then, within the same framework, I will discuss how social norms can evolve in the absence of a centralized authority. Finally, I will present some recent results on the modeling of the cryptocurrency market. Adopting an ecological perspective, I will show that the so-called neutral model of evolution reproduces key statistical properties of the market, despite the fact that it assumes no selective advantage of one cryptocurrency over another. These results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modeling and the study of this growing system.

20:30    Social Dinner

10.00 - 11.00    Focused Seminars II

Community Detection: a Primer

Clara Granell (Universitat de Barcelona, 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.

11.00 - 11:30    COFFEE BREAK

11:30 - 12:30    Focused Seminars II

Network Inference in Practice
Leto Peel (Université catholique de Louvain/ Université de Namur, Belgium)

An Introduction to Statistical Inference for Network Scientists
Statistical inference is an important tool for data analysis and network data is no exception! In this seminar, we will first introduce the popular probabilistic generative model, the stochastic block model (SBM) and its variants. Using the SBM we will explore a number of statistical inference methods for tasks such as inferring parameters, models selection, making predictions and hypothesis testing. Finally, we will briefly discuss some applications of these methods.

16.30 - 18.00     Project Presentations

18.00 - 19:30    Award and Closing Cerimonies