Complex Network Analysis [2016/17 Winter] | ||||
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CodeICNA |
NameComplex Network Analysis |
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Leistungspunkte8 LP |
Dauerone semester |
Turnusevery 2nd wintersemester |
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LehrformLecture 4 SWS, Exercise 2 SWS |
Arbeitsaufwand240 h; thereof 90 h lecture 15 h preparation for exam 135 h self-study and working on assignments/projects (optionally in groups) |
VerwendbarkeitB.Sc. Angewandte Informatik, M.Sc. Angewandte Informatik, M.Sc. Scientific Computing |
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Lernziel |
The students: - 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 |
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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 |
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Voraussetzungen |
recommended are: Algorithms and Data Structures (IAD), Knowledge Discovery in Databases (IKDD), Linear Algebra I (MA4) |
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Prüfungsmodalitäten |
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. |