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.

AIMS AND LEARNING OUTCOMES

At the end of the course, diligent students who have worked as instructed will have:

• acquired a basic understanding of some universal properties of graphs that can be used to study large networks, regardless of the application domain
• acquired a basic understanding of the evolution of large networks in the presence of failures or contagions
• learned some important ranking algorithms on graphs
• consolidated the theoretical knowledge of the topics seen during lectures, thanks to a series of exercises that will allow them to put into practice the theory seen in class

PREREQUISITES

To be successful in this course, students should have basic knowledge concerning:

• programming (for the practical activities)
• web (how it works, its structure)

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/)
• Scientific papers will be suggested during the course

TEACHERS AND EXAM BOARD

Exam Board

MARINA RIBAUDO (President)

MATTEO DELL'AMICO

GIOVANNA GUERRINI (Substitute)

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