AIMS AND CONTENT

LEARNING OUTCOMES

Learning algorithms and techniques for large scale graph analytics, including centrality measures, connected components, graph clustering, graph properties for random, small-world, and scale free graphs, graph metrics for robustness and resiliency, and graph algorithms for reference problems.

TEACHING METHODS

Lectures, practicals, and individual study.

SYLLABUS/CONTENT

Background on linear algebra and probability.

Complex networks introduction: examples from biology, sociology, economy, computer science.

Network topology (global and local level): connectivity, clustering, centrality measures, diameter, cliques, communities.

Graph models: random graphs, small-world, scale-free networks.

Graphs robustness and fault tolerance.

Web graph: Markov chains and random walk, ranking, search engines.

Dynamic evolution of graphs.

Epidemic models.

Case study: web, social media, epidemic models.

Complex data visualization using open source software tools.

RECOMMENDED READING/BIBLIOGRAPHY

M. E. J. Newman, Networks: An Introduction, Oxford University Press, Oxford (2010)
D. Easley and J. Kleinberg: Networks, Crowds, and Markets: Reasoning About a Highly Connected World (http://www.cs.cornell.edu/home/kleinber/networks-book/)
A. Barabasi: Network Science (http://barabasilab.neu.edu/networksciencebook/)
A. L. Barabasi, Link. La nuova scienza delle reti, Einaudi 2004 , introductory text (optional)

Scientific papers will be suggested during the course.

TEACHERS AND EXAM BOARD

Exam Board

MARINA RIBAUDO (President)

GIORGIO DELZANNO

GIOVANNA GUERRINI

LORENZO ROSASCO

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral examination with discussion of the practicals assigned during the course.

ASSESSMENT METHODS

Individual interview.

Exam schedule