Academic Year 2023-2024

STRUCTURE OF COMPLEX NETWORKS

Teachers

Dario Fasino
Unit Credits
6
Teaching Period
Second Period
Course Type
Supplementary
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. The lessons will be both theoretical and practical. The practical part is aimed at acquiring languages and software tools for analysing complex networks through case studies.
Verification of Learning. The grade is based on a written exam and a final oral exam. The written exam consists of computational problems, open questions, and small dissertations, aiming to evaluate the student’s knowledge and understanding of concepts and tools in network science. The oral exam includes some theory questions and the discussion of experimental activities performed individually on a topic chosen by the student, with the goal of evaluating the student’s ability to apply that knowledge in practical circumstances. The grading criteria are those established by the Course of Studies and can be found at the following link:

https://www.uniud.it/it/didattica/corsi/area-scientifica/scienze-matematiche-informatiche-multimediali-fisiche/laurea-magistrale/comunicazione-multimediale-e-tecnologie-dellinformazione/studiare/criteri.pdf

More Information. Learning resources available on the e-learning platform include handouts, lecture videos, lecture slides, and software resources. However, class attendance is strongly encouraged.
Objectives
* the student should have acquired the necessary knowledge to analyze and visualize structured (tabular and network) and free text data

* the student should have learned at least one software for the analysis and visualization of data especially for networks and text

* the student should be able to interpret experimental results and draw conclusions relevant to the domain of discourse.

* the student should be able to communicate effectively the results of an experimental analysis.

Contents
The course aims to provide students with an introduction to network science and in particular the analysis of real-world networks. Topics covered include network examples, elementary graph theory, 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, clustering, modularity, partitioning and community detection problems; traffic, diffusion and dynamic processes on networks.
Texts
(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.