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Programme of Course "Networks"

Code:

DT0341

Type of course unit:

Master Degree in Applied Data Science curriculum Data for Smart City: Compulsory
Master Degree in Applied Data Science curriculum Data for Life Science: Compulsory

Level of course unit:

Postgraduate Degrees

Semester:

2nd semester

Number of credits:

Master Degree in Applied Data Science: 6 (workload 150 hours)

Teachers:

Giovanni Stilo (giovannidotstiloatunivaqdotit)

1. Course Objectives

The student will be able to manage and analyze networks from several aspects.

2. Course Contents and learning outcomes (Dublin Descriptors)

Topics of the course include:

  • Fundamentals of graph theory and Notation
  • Linear Algebra and Norms
  • Node Similarity Measures and algorithms
  • Network Generators
  • Key Players of a Network and Centralities measures
  • Networks and communities: algorithms and metrics.

On successful completion of this course, the student should

  • On successful completion of this course, the student should: Understand: • Where graphs are, why they are important, and what are new applications; • The main challenges from data mining perspective:

Learn: • Analyze networks to understand the properties and the behaviors of individuals • Think in a research perspective (novelty,clarity,...) • Solve practical problems

3. Course Prerequisites

- Knowing at least one Programming Language. - Notions of linear algebra.

4. Teaching methods and language

Language:English[info]

Reference textbooks

  • Aggarwal, C.C. and Wang, H. eds, Managing and mining graph data. Springer. 2010.
  • Chakrabarti, D. and Faloutsos, C., Graph mining: laws, tools, and case studies.. 2012.
  • Easley, D. and Kleinberg, J., Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press. 2010.

5. Assessment Methods

Project, oral presentation of the project and discussion of course topics.

Course information last updated on: 06 giugno 2019, 09:58