Fundamental Ideas in Network Science

Course Description: 
Background and overall aim of the course:

This is an interdisciplinary course and students with different backgrounds are expected to register for it. The aim is to get acquainted with the basic concepts of network science, including elements of the theory of graphs, dynamics of and on networks, as well as with applications from biology, sociology, economics and other fields. Mostly elementary math prerequisites are assumed, the tools needed will constitute part of the course.

Detailed description:


1st lecture

2nd lecture







Elements of graph theory

Visualizing and measuring



Random graphs

Small worldness

Assignment 1


Scale freeness

Configuration model



Network growth models

Local structures




Robustness and vulnerability

Assignment 2 (midterm)


Random diffusion

Spreading phenomena



Temporal networks

Controlling networks



Multiplex networks

Signed networks



Social networks


WP article submission


Internet and WWW

Economics and finance



Ecological networks

Project presentation


Suggested reading:
  • M.E.J. Newman: Networks – An Introduction (Oxford UP, 2010)
  • A.-L. Barabási: Network Science (Cambridge UP, 2016)
  • J. P. Scott: Social Network Analysis: A Handbook (Sage Publications, 2004)
  • A. Barrat, M. Barthélemy and A. Vespignani: Dynamical Processes on Complex Networks (Cambridge UP, 2008)
  • D. Easley and J. Kleinberg: Networks, Crowds and Markets (Cambridge UP, 2010)

The pdf files of the lectures will be made available.

Teaching format:

The bulk of the course will be provided in lectures. There will be discussions of the tasks and the final projects will be presented by the students in a seminar form.

There will be regular homework assignments and two additional main assignments. Assignment 1 is to be prepared at home, the other one (midterm) is a classroom assignment.

Additional requirements:

Wikipedia article-type essay in the field of complex networks and final project. The tasks should be solved independently, except for the final project for non-DNDS students, who can work in pairs. Nevertheless, it is encouraged to form study groups.

E-learning and consultation hours:

The course has an e-learning site where materials about the lectures, homework, etc. are posted. It also serves for communication.

Consultation: Upon agreement.

Learning Outcomes: 

By successfully absolving the course the students will be able to:

  • Recognize the importance of the network approach in their own fields of studies;
  • Map out networks from data on complex systems in diverse fields of applications and use simple visualization softwares;
  • Carry out statistical analysis of complex networks regarding the basic characteristics;
  • Measure dynamic properties of processes on networks;
  • Attend the more specialized courses.
For grade:
  • Assignments (assignment 1: 10%, assignment 2: 20%)
  • Wikipedia article (20%)
  • Final project (carried out individually (for DNDS students) or in pairs (for others)) (40%)
  • Teacher evaluation (10%, mostly based on homework)
For audit:
  • Homework
  • Assignment 1 (completed at home)
  • Wikipedia article

Mostly elementary math prerequisites are assumed, the tools needed will constitute part of the course.