| Complex Network Analysis [2022/23 WiSe] | ||
|---|---|---|
| Code ICNA |
Name Complex Network Analysis |
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| LP 8 |
Dauer one semester |
Angebotsturnus every 2nd wintersemester |
| Format Lecture 4 SWS + Exercise course 2 SWS |
Arbeitsaufwand 240 h; thereof 90 h lecture 20 h preparation for exam 130 h self-study and working on assignments/projects (optionally in groups) |
Verwendbarkeit M.Sc. Angewandte Informatik M.Sc. Data and Computer Science M.Sc. Scientific Computing B.Sc. Mathematik |
| Sprache English |
Lehrende Michael Gertz |
Prüfungsschema |
| Lernziele | 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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| Lerninhalte | - 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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| Teilnahme- voraus- setzungen |
recommended are: Algorithmen und Datenstrukturen (IAD), Knowledge Discovery in Databases (IKDD), Lineare Algebra I (MA4) | |
| Vergabe der LP und Modulendnote | The module is completed with a graded written examination. The final grade of the module is determined by the grade of the examination. The requirements for the assignment of credits follows the regulations in section modalities for examinations. | |
| Nützliche 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. |
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