Structure and Dynamics of Complex Networks
Course code: CNSC 6007
The aim of this course is to deepen the knowledge of students in networks science, to get acquainted with some important specific problems at the frontier of research and get skills to work with present day literature.
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. Students are encouraged to consult the recommended literature.
There will be two assignments. Both assignments will consist of home prepared work. The students will have to prepare a project work and present in the last class.
Week 1st lecture
1 Introduction, block modeling
2 Community detection revisited
3 Core-periphery structures
7 Multiplex networks
Assignment 2 (midterm)
8 Link formation and prediction
9 Search on networks
10 Spreading I
11 Spreading II
12 Project presentation
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)
S. Fortunato: Community detection in graphs. Physics Reports 486, 75–174 (2010)
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,
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)
The course has an e-learning site where the materials about the lectures, assignments, etc. will be posted. It also serves for communication.
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.
- Assignments (assignment 1: 15%, assignment 2: 25%)
- Final project (50%)
- Teacher evaluation (10%)
Absolving “Fundamental Ideas in Network Science” is recommended. Basic knowledge of programming is required.