Program

15:00    Welcome

Meeting point at Catania airport with the School bus.

9:30 - 10:00    OPENING

Manlio De Domenico (Fondazione Bruno Kessler, Italy)
Alex Arenas (Universitat Rovira i Virgili, Spain)

10:00 - 11:00    Individual Presentations

11:00 - 12:00    Projects


SESSION I: STRUCTURE AND ROBUSTNESS OF COMPLEX NETWORKS

10:00 - 11:00    PART I

Ernesto Estrada (Universidad de Zaragoza, Spain)

STATIC CHARACTERIZATION OF COMPLEX NETWORKS
I will develop some mathematical concepts for the analysis of the structure of networks. I will make emphasis on problems detected with some network analytics and how to correct the, In particular I will discuss problems with degree distributions, small-worldness, clustering, and assortativity.

11:00 - 11:30    COFFEE BREAK

11:30 - 13:00    PART II

Albert Diaz-Guilera (Universidad de Barcelona, Spain)

COMMUNITY DETECTION AND DIFFUSION AS A LINK BETWEEN STATICS AND SPECTRAL PROPERTIES
We will present the topic of detection of communities in complex networks, with special emphasis in methods but also in uderstanding the role of communities in differents systems. With respect to the dynamicap properties we will stablish the relationship between diffusion dynamics, spectral properties and communities at different scales.


16:30 - 17:30    STUDENT TALKS

17:30 - 18:00    COFFEE BREAK

18:00 - 18:30    STUDENT TALKS



SESSION II: DYNAMICS ON COMPLEX NETWORKS

10:00 - 11:00    PART I

Ernesto Estrada (Universidad de Zaragoza, Spain)

NON-CLASICAL DIFFUSION
I will discuss some examples of non-classical diffusive processes on networks. I will discuss the use of generalized graph Laplacians, such as d-path Laplacians for diffusion on networks with long-range interactions. I will also introduce and discuss examples of hubs- repelling and hubs-attracting diffusive processes on networks.

11:00 - 11:30    COFFEE BREAK

11:30 - 13:00    PART II

Albert Diaz-Guilera (Universidad de Barcelona, Spain)

DYNAMICS: SYNCHRONIZATION, REACTION, AGENT BASED MODELLING
In this talk we will present non-linear dynamics in complex networks: sycnrhonization, reaction-diffusion, agent based models, …. with a final overview on the extension of these dynamical processes to multiplex and time dependent networks.


16:30 - 17:30    STUDENT TALKS

17:30 - 18:00    COFFEE BREAK

18:00 - 18:30    STUDENT TALKS



SESSION III: SPREADING PROCESSES IN COMPLEX NETWORKS

10:00 - 11:00    PART I

11:00 - 11:30    COFFEE BREAK

11:30 - 13:00    PART II


Sandro Meloni (IFISC, Spain)

In this lecture we will cover the fundamentals of contagion processes on networks along with current and hot topics in the area. In the first part of the lecture we will start by introducing basic concepts and measures for the modelling of epidemic spreading and follow by the study of standard spreading models on graphs. Then, we will explore matapopulations dynamics to tackle the analysis of spreading processes in realistic scenarios. The second part of the lecture will be devoted to current research topics such as: interacting and multi-strain diseases, epidemic spreading in multilayer and multiplex networks and real-time epidemic forecasting.

The outline of this lecture is:

PART I
- Introduction to epidemic modeling.
- The contact process and SIS dynamics on graphs.
- The heterogeneous mean field approximation.
- The quenched mean field approximation.
- Metapopulation models.

PART II
- Epidemic spreading in multilayer and multiplex networks.
- Interacting and multi-strain diseases.
- Vector-borne diseases.
- Epidemic forecasting.

16:00 - 20:00    SOCIAL BOAT

10.00 - 11.00    Focused Seminars I

COMMUNITY DETECTION IN COMPLEX NETWORKS
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.

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 - 17:30    Focused Seminars III

NETWORK NEUROSCIENCE
Joaquin Goñi (Purdue University, USA)

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.

17.30 - 18:00    COFFEE BREAK

18:00 - 19:00    Focused Seminars IV

COGNITIVE NETWORK ANALYSIS
Francesca Colaiori (CNR-ISC, ITALY)

Network science plays a significant role in cognitive science at different levels, from understanding brain processes to unvailing patterns of social and cultural dynamics. On the one hand, neural networks directly provide a map of the human brain. Multilayer networks can represent both structural and functional connectivity in a unified formalism. On the other hand, language systems, that are the most complex product of human cognition, allow for a network representation with linguistic units as nodes and edges given by linguistic relations of semantic, phonological, syntactic, or conceptual nature. Similarities in the structure of neural and linguistic networks suggest that mind associations between words and concepts are related to the way memories are physically stored and dynamically retrieved in the brain. Also, the topological and statistical properties of language networks give us clues on the processes that came into play in the evolution, organization, and categorization of knowledge and memories at the mental level, which is something that we cannot access directly. Language is not just the result of human performance at the individual level but also the product of a collective cognitive and social process. In each verbal exchange, the speaker aiming at communicating efficiently, chooses among several linguistic variants, maximizing understandability and minimizing effort under social and cognitive constraints. Individual behavior, such as word choice, speech perception errors or mispronunciation, affect the evolution of language at a population level and determine large-scale linguistic and cultural change. At this societal level, a key question is how physical constraints on memory and learning in human minds, reflect on the outcomes of cultural evolution, such as conventions and languages, in human societies.

20.30 - 22:00    social dinner

09:30 - 10.00    HUMAN BEHAVIOR I

COMPLEX NETWORKS TO UNDERSTAND HUMAN BEHAVIOR
PierLuigi Sacco

Social interactions present multiple challenges for complexity scholars, and they have been tackled from many different disciplinary angles. Network models have proven to be a powerful basis for a consilient, cross disciplinary approach to social complexity and much progress has been made in a variety of fields. In this talk, I will briefly review some of the most important conceptual issues that unify the literature on social complexity and will discuss to what extent the current literature has proven successful in addressing them and what are the still open problems. I will in particular cover the following issues: emergence of norms and conventions; social contagion; and social learning.

10.00 - 11:00    HUMAN BEHAVIOR II

MEASURING HUMAN BEHAVIOR: A NETWORK APPROACH
Daniela Paolotti (ISI Foundation, Italy)

The ever-growing digital world and the data it generates represent an unprecedented opportunity to harvest for tools that can help shed a light on human behavior. In particular, informal digital channels (e.g. social networking sites, Web searches, local news media etc.) have been credited with providing information on a wide variety of human behaviors that are not easily accessible through more traditional channels. Particularly in the domain of health, this digital means of information detection and collection have significantly changed the landscape of public health surveillance, epidemic intelligence gathering as well as the understanding of medical misinformation spreading. More broadly, digital data can also be used to quantitatively address some societal challenges, e.g. social and gender inequalities, migrations and displacement, and to provide new methodologies and solutions to inform policy making. In this lecture, I will present some examples of how digital data can be used to monitor population health and health-related lifestyles and behaviors, as well as to study high-impact societal issues such as poverty and gender inequalities, with a focus on methodologies derived from complex systems and complex networks.

11.00 - 11:30    COFFEE BREAK

11:30 - 12:30    HUMAN BEHAVIOR III

COLLECTIVE PHENOMENA: SOCIAL NORMS AND THEIR DYNAMICS IN COMPLEX SOCIETIES
Giulia Andrighetto (CNR-ISTC, Italy)

Lives of human beings are largely governed by social norms, rules or principles that, independently from legal institutions, prescribe what we ought or ought not to do. Scholars from different fields have recognized the importance of social norms as solutions to major local and large-scale collective action problems like management of climate change, ecosystem and habitat destruction, and the decline of vaccinations. Despite their importance, the empirical foundation of social norms is still limited.
In this talk, I will present work on the emergence and change of social norms and their effect in promoting human cooperation. I will discuss results from recent laboratory and simulationexperiments showing that social norms are causal drivers of behavior and can explain cooperation-related regularities

> What are social norms.
> How to measure social norms.
> Dynamics of social norms (how norms may emerge, become stable, and change).
> Effect of social norms on human social behavior.

16.30 - 18.00     PROJECT PRESENTATIONS

18.30 - 18.45    COFFEE BREAK

18.45 - 19:45     AWARD AND CLOSING CERIMONIES