Vorlesung/Praktikum Complex Network Analysis mit GNU R

Vorlesung und Praktikum (19620) – 2+2-stündig, ECTS: 5

Dozent:	Claudia Müller-Birn
Zeitraum:	20.02.2012 bis 02.03.2012, Takustraße 9, SR 046
Haupttermine:	Blockkurs: vormittags Vorlesung 10:00-12:00 Uhr, nachmittags Praktikum von 12:00 – 14:00 Uhr
Inhalt:	Die Veranstaltung gibt eine Einführung in R, eine Programmiersprache und Softwareumgebung für statistisches Rechnen und Grafiken. Wir werden uns in diesem Kurs insbesondere mit igraph, einer Bibliothek zur Analyse komplexer Netzwerke, auseinandersetzen.Unter Nutzung von Datensätzen, wie beispielsweise von Twitter, Enron E-Mail Kommunikation, Web Graph oder GNOME, werden wir neben den theoretischen Grundlagen zur Analyse von Netzwerken auch die dafür notwendigen Funktionen kennenlernen. Ein theoretischer Schwerpunkt wird auf dem Bereich Community Detection liegen.Teilnehmer des Kurses werden Erlerntes selbstständig an einem speziellen Datensatz anwenden und schrittweise bestehende Eigenschaften aufdecken.
Literatur:	Crawley, Michael J.: The R book. Wiley, 2007. Spector, Phil: Data Manipulation with R. Springer, 2008. Newman, Mark: Networks: An Introduction. Oxford University Press, 2010. Dorogovtsev, Sergey: Lectures on Complex Networks, Oxford University Press, 2010.
Leistungs- nachweise	Assignment 1 (homework) Homework assignments are due to the beginning of the following lecture It is worth 50 % of final mark Assignment 2 (class project) Giving a talk (presentation based on slides) about current state of class project Describing the theoretical context and analyzing the assigned data Reporting results in class report (five pages) with extensive discussion It is worth 50% of final mark Please note: If you have difficulties to collect your data, please use the following provided networks (please check, the number of vertices should be the same because they belong together.). To pass this course you must score at least 50% on each assignment.

Day	Lecture	Lab	Assignment
Mon 2012-02-20	Basics of descriptive statistics (slides)	Basics of GNU R (part I) (slides)	Homework: Basics statistics (part I) (data)
Tues 2012-02-21	Networks: basic concepts, definitions, models (slides)	Basics of GNU R (part II) (slides)	Homework: Basics statistics (part II)
Wed 2012-02-22	Describing network structure on a local level (slides updated)	Introducing the igraph library (slides)	Homework: Basic network measures (data)*
Thu 2012-02-23	Describing network structure on a global level (slides)	R: sapply function, generic functions Computing network measures with igraph Writing your own functions (slides updated)	Homework: Basic measures part II
Fri 2012-02-24	Community detection in networks (slides updated)	R: tapply function, lists, factors Calculating modularity in igraph (slides)	Homework: Modularity
Mon 2012-02-27	Network visualization Preparing class project (slides) – in 7z format	Topics upon request	Class project
Tues 2012-02-28	Parameters for defining temporal networks (slides)	How to request data from MediaWiki API (slides)	Class project
Wed 2012-02-29	Presenting and discussing status of class projects
Thus 2012-03-01	Diffusion in networks (slides)	Topics upon request	Class project
Fri 2012-03-02	Project day

* After importing the network you should apply the following function „g <- simplify(g)“. This function deletes all multiple edges (Two directed edges are multiple edges if they have exactly the same two endpoints) and all loops in the graph.

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