NETWORK ANALYSIS

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Code
90530
ACADEMIC YEAR
2021/2022
CREDITS
6 credits during the 1st year of 10852 COMPUTER SCIENCE (LM-18) GENOVA

6 credits during the 2nd year of 9011 Mathematics (LM-40) GENOVA

SCIENTIFIC DISCIPLINARY SECTOR
INF/01
LANGUAGE
English
TEACHING LOCATION
GENOVA (COMPUTER SCIENCE )
semester
2° Semester
Teaching materials

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

TEACHERS AND EXAM BOARD

Office hours: By appointement at the DIBRIS Department, room 231, 2nd floor, Via Dodecaneso 25, Genova. Online on Teams in case of distance learning. E-mail: marina.ribaudo@unige.it  

Exam Board

MARINA RIBAUDO (President)

GIOVANNA GUERRINI

GIORGIO DELZANNO (President Substitute)

LORENZO ROSASCO (Substitute)

LESSONS

TEACHING METHODS

Lectures, practicals, and individual study.

Class schedule

All class schedules are posted on the EasyAcademy portal.

EXAMS

EXAM DESCRIPTION

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

ASSESSMENT METHODS

Individual interview.

Exam schedule

Date Time Location Type Notes
07/01/2022 09:00 GENOVA Esame su appuntamento
08/06/2022 09:00 GENOVA Esame su appuntamento
22/07/2022 09:00 GENOVA Esame su appuntamento
08/09/2022 09:00 GENOVA Esame su appuntamento
16/09/2022 09:00 GENOVA Esame su appuntamento
10/02/2023 09:00 GENOVA Esame su appuntamento