|Complex Network Analysis [2016/17 Winter]|
Complex Network Analysis
every 2nd wintersemester
Lecture 4 SWS, Exercise 2 SWS
240 h; thereof
90 h lecture
15 h preparation for exam
135 h self-study and working on assignments/projects (optionally in groups)
B.Sc. Angewandte Informatik,
M.Sc. Angewandte Informatik,
M.Sc. Scientific Computing
- can describe basic measures and characteristics of complex networks
- can implement and apply basic network analysis algorithms
- can describe different network models and can describe, compute, and analyze characteristic parameters of these models
- know how to compute different complex network measures and how to interpret these measures
- know different generative models for constructing complex networks, especially scale-free networks
- know the fundamental methods for the detection of communities in networks and the analysis of their evolution over time
- are familiar with basic concepts of network robustness
- understand the spread of phenomena in complex networks
|Inhalt||- Graph theory and graph algorithms; basic network measures
- Random networks and their characteristics (degree distribution, component sizes, clustering coefficient, network evolution), small world phenomena
- Scale-free property of networks, power-laws, hubs, universality
- Barabasi-Albert model, growth and preferential attachment, degree dynamics, diameter and clustering coefficient
- Evolving networks, Bianconi-Barabasi model, fitness, Bose-Einstein condensation
- Degree correlation, assortativity, degree correlations, structural cutoffs
- Network robustness, percolation theory, attack tolerance, cascading failures
- Communities, modularity, community detection and evolution
- Spreading phenomena, epidemic modeling, contact networks, immunization, epidemic prediction
|Voraussetzungen||recommended are: Algorithms and Data Structures (IAD),
Knowledge Discovery in Databases (IKDD), Linear Algebra I (MA4)
|Assignments; at least 50% of the credit points for the assignments need to be obtained to be eligible to participate in the final written exam; students can also work on project (non-graded); final written exam|
|Literatur||Albert-Laszlo Barabasi: Network Science, Cambridge University Press, 2016.
M.E.J. Newmann: Networks: An Introduction, Oxford University Press, 2010.
Reza Zafarani, Mohammad Abbasi, Huan Liu: Social Media Mining-An Introduction, Cambridge University Press, 2014.
David Easley, Jon Kleinberg: Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010.
Stanley Wasserman, Katherine Faust: Social Network Analysis-Methods and Applications, Cambridge University Press, 1994.