Complex Network Analysis [2019 Sommer] | ||||
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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 course 2 SWS |
Arbeitsaufwand240 h; thereof 90 h lecture 12 h preparation for exam 130 h self-study and working on assignments/projects (optionally in groups) |
VerwendbarkeitB.Sc. Angewandte Informatik, M.Sc. Angewandte Informatik, M.Sc. Scientific Computing B.Sc. Mathematik |
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Lernziel |
Students - can describe basic measures and characteristics of complex networks - can implement and apply basic network analysis algorithms using programming environments such as R or Python - 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 principles behind 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: Algorithmen und Datenstrukturen (IAD), Knowledge Discovery in Databases (IKDD), Lineare Algebra I (MA4) | |||

Prüfungsmodalitäten |
final written exam | |||

Literatur |
- Albert-Laszlo Barabasi: Network Science, Cambridge University Press, 2016. - M.E.J. Newmann: Networks: An Introduction, Oxford University Press, 2010. - Vito Latora, Vincenzo Nicosia, Giovanni Russo: Complex Networks - Principles, Methods and Applications, Cambridge University Press, 2017. - 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. |