Academic Year 2019-2020



Dario Fasino
Unit Credits
Teaching Period
Second Period
Course Type
Prerequisites. Prerequisites include adequate, graduate-level background in algorithms, graphs, probability, and linear algebra (matrix calculus, linear equations, eigenvalues and eigenvectors), as those given in the “Graph theory and game theory” course.
Teaching Methods. Classroom lessons, lab sessions.
Verification of Learning. Grade is based on a written exam and a final oral exam. The written exam consists of computational problems, open questions, and small dissertations, in order to evaluate the student’s knowledge and understanding

of concepts and tools in network science. The oral exam includes the discussion of lab activities, with the goal of evaluating the student’s ability to apply that knowledge in

practical circumstances.

More Information. Learning resources available on the e-learning platform include handouts, lecture slides, and software resources. However, class attendance is strongly encouraged.
On completion of this course students should – know the most common metrics, algorithms, and techniques of complex network analysis and classification. – be able to recognize the structure, evolution, dynamics, and criticalities of complex networks in real world – be able to report on structure, role and relevance of elements in a complex network by making appropriate use of software tools, quantitative evaluations and graphical representations.
Network examples, global network properties and invariants, distance and centrality measures, generative models (random, small-world, scale-free), random walks, Markov chains, Perron-Frobenius theory, PageRank and HITS algorithms, assortativity and clustering, partitioning and community detection problems.
(1) M. Newman. Networks: An Introduction. Oxford University Press, 2010. (2) E. Estrada, P. Knight. A first course in network theory. Oxford 2015. (3) Teacher’s lecture notes.