Complex Networks for Data Science

Provided by: 

IMT

Sede: 

IMT Lucca

Lecturers: 

Guido CALDARELLI, T. SQUARTINI, G. CIMINI

Semester: 

2

Hours: 

40

Exam: 

Y

Educational Goals: 

The course aims at providing an overview of methods to analyse complex networks.

Prerequisites: 

Solid mathematical background, scientific curiosity, interest in multidisciplinarity, passion for theory.

Programme: 

Part I - Introduction to Complex Networks (Graph Theory Introduction. Properties of Complex Networks. Community Detection. Ranking Algorithms. Static Models of Graphs. Dynamical Models of Graphs. Fitness Models. Financial Networks). Part II - Algorithms and Applications (Centrality Measures. Spectral Properties of Graphs. Community Detection. Bipartite Networks. Ranking and Reputation Algorithms. Trade Network Datasets. Multilayer Networks. Infrastructural Networks). Part III - Statistical Mechanics of Networks (Complex Networks Randomization: A Primer. Basics of Information Theory. The Exponential Random Graphs Framework: From Zero to Shannon. The Maximum-Likelihood Recipe for Parameters Estimation. Hypothesis Testing on Networks: Pattern Detection, Network Filtering, Network Projection. The Dutch Interbank Network Case-Study. Network Reconstruction: A Survey of Existing Methods. Network Reconstruction: Moving Towards Entropy-Based Recipes. The World Trade Web Case-Study. International Economic Networks: The Interplay between Trade, Finance, Production and Migrations). Part IV - Dynamical Processes on Networks (Master Equations, Models of Growing Networks - Continuous Description. Epidemics. Scaling and Percolation on Networks. Contagion in Financial Networks. Game Theory).
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