Social Computing

What it is about

You learn the basics and methods of Social Computing: concepts of social media, social networking and social contexts as well as sociometric methods (centrality, density, clustering), metrics of real networks, models of social relationships in space and time, Social Signal Processing and fundamentals of game theory. In the end you will be able to apply these concepts to the conception, implementation and research of Social Computing applications.

What you will be able to do

Understand basic terms and concepts of Social Computing
Apply sociometric methods (centrality, density, clustering)
Analyze metric properties and models of real networks
Use models of social relationships in space and time
Apply fundamentals of Social Signal Processing and game theory
Read and classify Fachpublikationen (specialist publications)
Develop own algorithms and sociotechnical solution approaches

What the module consists of

VorlesungConveying the basics and concepts of Social Computing
praktische ÜbungenFamiliarization and application of the concepts; capturing and analyzing data, evaluation with own programs and third-party software; in small groups or individually

Teaching method

VorlesungIntroduction to concepts, models and algorithms
praktische Übungen (blockweise)Practice and application of the lecture content on data sets; presentation of results
E-Learning Foren supervision of the exercises and discussion

What you should already know

Inhalte des Bachelors Informatikrecommended

Assessment

Written examvoluntary graded MidtermIPython taskswritten

The examination performance is a 90-minute written exam with 25 tasks (10 points each), testing knowledge and competences such as recalling, linking, applying and evaluating. In addition, there is a voluntary graded midterm consisting of six IPython task sheets (project character, electronically submitted, one week per task). The midterm is only credited if it improves the exam grade; it can influence the overall grade by up to 50% or, with an average of 2.0 or better, grant a bonus of 0.3.

Reading list

Henry Pötzl - A Review on Social Games and Mobile Social Games (Master’s thesis, TUM Informatics, 2012)
Georg Groh - Contextual Social Networking (Habilitation thesis, TUM Informatics, 2018)
D. Koschützki, K. Lehmann, L. Peeters, S. Richter, D. Tenfelde-Podehl, O. Zlotowski - Centrality Indices (in U. Brandes, T. Erlebach (Eds.) Network Analysis, LNCS 3418, Springer, 2004)
Mark E. J. Newman - A measure of betweenness centrality based on random walks (arXiv cond-mat/0309045, 2003)
Sven Kosub - Local Density (in U. Brandes, T. Erlebach (Eds.) Network Analysis, LNCS 3418, 2004)
M. Gaertler - Clustering (in Brandes, Erlebach (eds.) Network Analysis, LNCS 3418, Springer, 2005)
M. Newman - The structure and function of complex networks (SIAM Review, 2003)
C. Bishop - Pattern Recognition and Machine Learning (Springer, 2006), Kapitel 9
D. Fudenberg, J. Tirole - Game Theory (MIT Press, 1991), Kapitel 1
Michael Wooldridge - An Introduction to Multi Agent Systems, 2nd edition (Wiley, 2009), Kapitel 11
David Liben-Nowell - Wayfinding in Social Networks (in Algorithms for Next Generation Networks, Springer, 2010)
A. Vinciarelli, M. Pantic, H. Bourlard - Social signal processing: Survey of an emerging domain (Image and Vision Computing, 27(12), 2009)

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Social Computing

What it is about

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