(last updated May 13, 2022)
[time zone UTC+2 – check your time lag at https://www.timeanddate.com/time/zone/italy]
Monday, 16 May, morning (9.30-12.30)
Introduction to complex networks (Sales-Pardo): In this introductory lecture, I will talk about general properties of networks. We will look into diferent kinds of networks and how to characterize the structure of these networks. We will also make a walk in the history of the analysis of the large-scale structure of networks.
Monday, 16 May, afternoon (14.30-17.30)
Science informing COVID-19 policies (remote session: Vittoria Colizza, Yamir Moreno, Niel Hens, Jacco Wallinga, Alessia Melegaro): Through experiences from different countries, the session will explore how science helped shaping policies to manage COVID-19 pandemic, addressing issues of timing, reliability, communication, academic research, and existing gaps.
Tuesday, 17 May, morning (9.30-12.30)
Inference for complex networks (Sales-Pardo): I will introduce the ideas behind probability theory and how these probabilistic approaches can help us extract knowledge from data. In particular, I will focus on the use of Baysian inference approaches to the problems of extracting the large-scale structure of complex networks, the problem of predicting missing data and the problem of network reconstruction.
Tuesday, 17 May, afternoon
no lectures
Wednesday, 18 May, morning (9.30-12.30)
Computational social science (Lehmann): As harddrives have gotten less and less expensive and database software ubiquitous over the past two decades, humanity has collected more data about human behavior than ever before. These new datasets hold the promise of new discoveries about the principles and patterns that govern and shape human choices and actions – a data driven social science. With a starting point in my own research, I discuss advances over the past ten years, covering networks, human mobility, sleep, and social media. The lecture also addresses methodological developments.
Wednesday, 18 May, afternoon (14.30-17.30)
Computational human dynamics (Karsai): Human actions and interactions appear neither deterministic nor completely random for an external observer. They are driven by several confounding factors like personal decisions and preferences, inter-personal influence, or impulses arriving from the environment just to mention a few. Consequently, their characterization, modeling and understanding have to consider simultaneously their stochastic but correlated nature, which can be conveniently approached by computational methods. Early simulation studies of human dynamics focused on the mechanistic modeling of emergent social phenomena like the social structure or any collective process taking place on it. Multivariate statistical models have been also developed to identify correlations and causal patterns in the temporal sequence of decisions, mobility, or adoption patterns of individuals or groups. However, the recent availability of large digitally behavioral datasets radically changed this landscape and opened up novel opportunities for the application and development of computational methods borrowed from statistical learning and artificial intelligence. This lead researcher to achieve better understanding of human behavior, and to build predictive models with ever seen precision about processes driven by the many aspects of human dynamics. In this lecture we are going to discuss examples regarding these aspects of computational human dynamics. We will identify some ways human dynamical data can be collected and will introduce several computational models, built on mechanistic or statistical learning conventions, to describe human dynamics at the individual, group and collective level. More precisely, we will discuss the characterization and potential explanations of bursty patterns of individual dynamics; we will focus on temporal networks and their different ways of modeling and representations; and we will see how statistical learning methods can be use to infer characteristics of individuals like their language usage or socioeconomic status.
Wednesday, 18 May, evening (20.00)
social dinner
Thursday, 19 May, morning (9.30-12.30)
short talks by students: click for the schedule of the short talk session
Thursday, 19 May, afternoon
no lectures
Friday, 20 May, morning (9.30-12.30)
Biological networks (Pržulj): Dealing with complex “omic” data is computationally intractable. Hence, we must develop methods for extracting new biomedical knowledge from them. In this lecture, we will present new computational methods from our lab to address these challenges. Our new computational methods uncover the patterns in molecular networks and in the multi-scale network organization indicative of biological function, translating the information hidden in the network topology into biomedical knowledge. Also, we introduce a versatile data fusion (integration) framework to address key challenges in precision medicine: better patient stratification, prediction of driver genes in cancer, and re-purposing of approved drugs to particular patients and patient groups. Our new methods stem from novel network science approaches coupled with machine learning, such as graph-regularized non-negative matrix tri-factorization. We utilize our new methodologies for performing other related tasks, including uncovering new cancer mechanisms and disease re-classification from modern, heterogeneous molecular level data, inferring new Gene Ontology relationships, and aligning multiple molecular networks.
Friday 20 May, afternoon (14.30-17.30)
Brain networks (De Vico Fallani): 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 modeling temporally dynamic brain networks to how integrating 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.