Complex Network Analysis [2022 Sommer]  

Code ICNA 
Name Complex Network Analysis 

LP 8 
Dauer one semester 
Angebotsturnus every 2nd wintersemester 
Format Lecture 4 SWS + Exercise course 2 SWS 
Arbeitsaufwand 240 h; thereof 90 h lecture 12 h preparation for exam 130 h selfstudy 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 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 principles behind 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: 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 exam. The final grade of the module is determined by the grade of the exam. The requirements for the assignment of credits follows the regulations in section modalities for exams.  
Nützliche Literatur   AlbertLaszlo 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 AnalysisMethods and Applications, Cambridge University Press, 1994. 