(updated March 21, 2016)
Monday, 16 May, morning (9.30-12.30)
Structural network theory: Traditional vs. non-traditional methods (Estrada): Introduction to degree distributions, assortativity, communication by shortest paths, etc. Difficulties for their application, implementation and applications. Non-traditional methods based on algebraic, graph-theoretic and topological approaches. Answering questions about: How to compare degree heterogeneities in the presence of scarce data? What is the structural meaning of degree assortativity? How can you navigate a network without knowing the shortest paths?, etc.
Monday, 16 May, afternoon
Tuesday, 17 May, morning (9.30-12.30)
Spatial networks: theory and applications (Barthélemy): Characterization of spatial networks: tools and some important null models. Empirical measures and application to urban systems: time evolution of road, subway and railway networks; mobility networks in large cities.
Tuesday, 17 May, afternoon (14.30-17.30)
Temporal networks (Barrat): Introduction. Empirical data and characterization of temporal networks (metrics, structures). Comparison with randomized data sets (null models). Models of temporal networks. Using temporal network data in data-driven models of epidemic processes.
Wednesday, 18 May, morning (9.30-12.30)
Mesoscale structures in networks (Porter): Summary. Introduction. Community structure. Roles and positions. Block models. Stochastic block models. Core-periphery structure. Extensions when considering temporal, multilayer, and spatial networks.
Wednesday, 18 May, afternoon (14.30-17.30)
Statistical inference of generative network models (Peixoto): Fundamental generative models: exponential random graphs; stochastic block models; latent space models. The stochastic block model (SBM): microcanonical vs canonical models; degree-correction; optimal inference; belief propagation; indetectability transition; efficient Monte-Carlo algorithms. Model selection: Occam’s razor; Bayesian inference and the minimum description length principle; prior information and the resolution limit; the nonparametric hierarchical SBM. Layered and temporal SBMs; generalized community structure. Prediction of spurious and missing links.
Thursday, 19 May, morning (9.30-12.30)
Social and economical networks from (big-)data (Moro): Introduction to geo/social/economical (big-)data. Modeling human behavior at society scale. (Big-)data tools for Network Analysis. Applications and examples. Open problems and future challenges.
Thursday, 19 May, afternoon
Friday, 20 May, morning (9.30-12.30)
The human structural connectome: organisation, development, and dynamics (Kaiser): The lecture covers development and evolution, hierarchical and modular organisation, and structure-dynamics relationships of structural (anatomical) human brain connectivity. Some time is also devoted to practical exercises with Matlab on how to analyse brain networks.
Friday, 20 May, afternoon (14.30-17.30)
Functional brain networks (Buldú): Functional networks account for the neurodynamical interactions between neural regions. In this lecture, we will overview how network science is used to analyse functional brain networks. Specifically we will focus on: (i) how to construct networks from brain activity and (ii) how to analyse functional networks. Finally, we will discuss about what are the current limitations of network science applied to the brain analysis and suggest alternative approaches.