Structure and Dynamics of Complex Networks

Campus: 
Vienna
Academic Year: 
2019-2020
Term: 
Winter
US Credits: 
2
ECTS Credits: 
4
Course Code: 
DNDS 6004
Course Description: 

Description

The aim of this course is to deepen the knowledge of students in network science, to get acquainted with some important specific problems at the frontier of research and get skills to work with present day literature.

Course schedule

Week1st lecture + assignmentInstructor
1Introduction, block modellingJános Kertész
2Community detection revisitedFederico Battiston
3Games on networksFederico Battiston
4Hierarchies. Assignment 1János Kertész
5OptimizationJános Kertész
6SynchronizationFederico Battiston
7Multiplex networks. Assignment 2 (mid-term)Federico Battiston
8Link formation and predictionJános Kertész
9Search and exploration on networksFederico Battiston
10Spreading phenomenaJános Kertész
11CascadesJános Kertész
12Project presentationJános Kertész, Federico Battiston

Suggested reading

Books:

M.E.J. Newman: Networks – An Introduction (Oxford UP, 2010)

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)

Articles:

S. Fortunato: Community detection in graphs. Physics Reports 486, 75–174 (2010)

S. Fortunato and D. Hric: Community detection in networks: A user guide. Physics Reports 659, 1–44 (2016)

M. P. Rombach et al.: Core-periphery structure in networks, SIAM J. App. Math. 74, 167 (2014)

B. Corominas-Murtra et al: On the origins of hierarchy in complex networks, PNAS 110, 13316 (2013)

S. Boccaletti et al. Structure and Dynamics of Multilayer Networks, Physics Reports 544, 1, (2014)

M. Kivela et al. Multilayer networks, Journal of Complex Networks 2, 203 (2014)

G. Kossinets and D. Watts: Origins of homophily in evolving networks, Amer. J. Sociol. 115, 405 (2009)

D. Liben-Nowell and J. Kleinberg: The link-prediction problem for social networks, J. Amer. Soc. Information, Sci. and Technol. 58, 1019 (2007)

R. Pastor-Satorras et al.: Epidemic processes on complex networks, Rev. Mod. Phys. 87, 925 (2015)

Teaching format

The bulk of the course will be provided in lectures. Preparation for the classes involves the reading of selected papers, which will be discussed. The individual project consists of working through a paper and preparing a 15-minute talk to be presented in the last class.

E-learning

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

Learning Outcomes: 

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

  • get orientation in the present day literature in network science;
  • get insight into specific problems and work on them;
  • be able to identify specific research problems in network science and get access to tools needed to solve them.
Assessment: 
There will be two assignments. Both assignments will consist of home prepared work. The students will have to prepare a piece of project work and present in the last class.
  • Assignments (assignment 1: 15%, assignment 2: 20%)
  • Homework (15%)
  • Final project (40%)
  • Teacher evaluation (10%)
Prerequisites: 

Absolving DNDS 6000 Fundamental Ideas in Network Science is recommended. Basic knowledge of programming is required.