Complex Network Analysis [2016/17 Winter]  

Code ICNA 
Name Complex Network Analysis 

LP 8 LP 
Dauer one semester 
Angebotsturnus every 2nd wintersemester 
Format Lecture 4 SWS, Exercise 2 SWS 
Arbeitsaufwand 240 h; thereof 90 h lecture 15 h preparation for exam 135 h selfstudy and working on assignments/projects (optionally in groups) 
Verwendbarkeit B.Sc. Angewandte Informatik, M.Sc. Angewandte Informatik, M.Sc. Scientific Computing 
Sprache 
Lehrende 
Prüfungsschema 
Lernziele  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 scalefree 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 

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  Scalefree property of networks, powerlaws, hubs, universality  BarabasiAlbert model, growth and preferential attachment, degree dynamics, diameter and clustering coefficient  Evolving networks, BianconiBarabasi model, fitness, BoseEinstein 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 

Teilnahme voraus setzungen 
recommended are: Algorithms and Data Structures (IAD), Knowledge Discovery in Databases (IKDD), Linear Algebra I (MA4) 

Vergabe der LP und Modulendnote  Bestehen der Modulprüfung  
Nützliche Literatur  AlbertLaszlo 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 MiningAn 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 AnalysisMethods and Applications, Cambridge University Press, 1994. 