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:
Week  1st lecture  2nd lecture  Comments 
1  Introduction  Percolation 

2  Elements of graph theory  Visualizing and measuring 

3  Random graphs  Small worldness  Assignment 1 
4  Scale freeness  Configuration model 

5  Network growth models  Local structures 

6  Communities  Robustness and vulnerability  Assignment 2 (midterm) 
7  Random diffusion  Spreading phenomena 

8  Temporal networks  Controlling networks 

9  Multiplex networks  Signed networks 

10  Social networks  Mobility  WP article submission 
11  Internet and WWW  Economics and finance 

12  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 articletype essay in the field of complex networks and final project. The tasks should be solved independently, except for the final project for nonDNDS students, who can work in pairs. Nevertheless, it is encouraged to form study groups.
Elearning and consultation hours:
The course has an elearning site where materials about the lectures, homework, etc. are posted. It also serves for communication.
Consultation: Upon agreement.
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.