OVERVIEW

Networks are everywhere and they can be represented as graphs. This course will unravel the hidden patterns and dynamics of networks in various domains such as social networks, internet and the web, biological systems, and more. Students will learn the theoretical foundations of network analysis and experience hands-on activities to put into practice what introduced during classes.

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, students will be able to:

EXPLAIN universal properties of graphs that can be used to study large networks, regardless of their application domains
​EXPLAIN popular ranking algorithms
UNDERSTAND which synthetic model represents best a real network
DISCUSS the evolution of large networks in the presence of failures or contagions
USE available libraries to IMPLEMENT exercises and put into practice the topics seen during lectures

PREREQUISITES

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

• basic graph theory (definitions, paths, components, visits)
• web (how it works, its structure)
• programming (for the hands-on activities)

TEACHING METHODS

Lectures, hands-on activities, and individual study.

SYLLABUS/CONTENT

Students will learn how to analyze graphs of large size, even when it is impossible to visualize them because they are too big. Topics covered during the course are the following:

• Background on linear algebra and probability
• Complex networks introduction: examples from biology, sociology, economy, computer science
• Network topology (at local and global level): degree, centrality measures, connectivity, communities, and more
• Network models: random graphs, small-world, scale-free networks
• Web graph: Markov chains and random walk, ranking, search engines
• Robustness and fault tolerance of networks (random failures and target attacks)
• Dynamic evolution of networks (social contagion and epidemic spreading)
• Networks visualization using open source software tools

RECOMMENDED READING/BIBLIOGRAPHY

• F. Menczer, S. Fortunato, C. A. Davis: A First Course in Network Science, Cambridge University Press, 2020
• 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

LESSONS START

In agreement with the calendar approved by the Degree Program Board of Computer Science.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral examination with discussion of the:

• practical exercises assigned during the course
• theory introduced during lectures

ASSESSMENT METHODS

During the oral exam students will be evaluated based on:

• the quality of the produced code and the completeness of the reports
• their understanding of the theoretical concepts covered in the course
• their presentation skills

Exam schedule