Complex Network Analysis

PhD course @ UAB (2022)

Where: Universitat Autònoma de Barcelona, P.h.D in Economia Applicada
Duration: 12 hours
Who(‘s teaching): Dr. Giulio Rossetti

Course Description

Over the past two decades there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.

This crash course is an introduction to the analysis of complex networks, made possible by the availability of big data, with a special focus on the social network and its structure and function. Drawing on ideas from computing and information science, complex systems, mathematic and statistical modeling, economics, and sociology, this lecture sketchily describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

Course Structure

Lecture 1: Introduction to Network Analysis: Measures and fundamentals

  • Chapter 0. Why should we care about Complex Networks?
  • Chapter 1. Networks & Graphs: Basic Measures

Lecture 2: Characterizing by contraposition: Real Networks and Synthetic Models

  • Chapter 2. Random Networks
  • Chapter 3. It’s a Small World!
  • Chapter 4. Scale Free Networks

Lecture 3: Micro, Meso & Macro: Different perspectives

  • Chapter 5. Micro: Centrality & Tie Strength
  • Chapter 6. Meso: Community Discovery
  • Chapter 7. Macro: Assortativity & Resilience

Lecture 4: Dynamics of Networks: Topology perturbations

  • Chapter 8. Representing Dynamic Topologies
  • Chapter 9. Dynamic Community Discovery
  • Chapter 10. Link Prediction

Lecture 5: Dynamics on Networks: Diffusive phenomena

  • Chapter 11. Epidemics
  • Chapter 12. Opinion Dynamics

Lecture 6: Hands-on!

Materials