Network Modeling (Winter 2013/2014)
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Note: time and location of tutorials have been changed to |
Social network analysis, i.e., the joint analysis of actors and relations among them, rapidly gains importance in many scientific and commercial applications. Examples range from studies of organizational and communication networks over to the analysis of Web-based user interaction. Statistical approaches in social network analysis are applied to model, estimate, and predict social interaction and behavior based on empirical data. In this course you will learn mathematical and methodological foundations for modeling social networks. In the first part we treat models for time-independent networks and in the second part we model the evolution of networks over time. This course is part of a set of three related lectures offered by the Algorithmics group: Network Analysis, Network Dynamics, and Network Modeling. Note that these courses can be taken independently of each other and in any order. Prerequisites Good knowledge of basic mathematical concepts, as well as strong mathematical soft skills, i.e., the ability to understand and work with mathematical definitions, theorems, and proofs. |
Schedule
| Lecture (Viviana Amati & Jürgen Lerner) | Wed 13:30-15:00 in E 403 |
| Tutorial (David Schoch) | Wed 17:00-18:30 in E 403 |
| Exams (oral) | 12 February 2014 (by individual appointment) in E 203 |
Exercises
Most documents are only locally accessible - see possibilities for remote access.New assignments will be placed online in the evening after the lecture.
Solutions are due on Tuesday at 12:00.
Place solutions in the box in front of E203, or send an email with attached pdf to David.schoch@uni-konstanz.de.
| no. | online | due | tutorial | download |
|---|---|---|---|---|
| 0 | 23.10.13 | 28.10.13 | 28.10.13 | |
| 1 | 30.10.13 | 05.11.13 | 06.11.13 | |
| 2 | 06.11.13 | 12.11.13 | 13.11.13 | |
| 3 | 13.11.13 | 19.11.13 | 20.11.13 | |
| 4 | 20.11.13 | 16.11.13 | 27.11.13 | |
| 5 | 27.11.13 | 03.12.13 | 04.12.13 | |
| 6 | 04.12.13 | 10.12.13 | 11.12.13 | |
| 7 | 11.12.13 | 17.12.13 | 18.12.13 | |
| 8 | 18.12.13 | 07.01.14 | 08.01.14 | |
| 9 | 08.01.14 | 14.01.14 | 15.01.14 | |
| 10 | 15.01.14 | 21.01.14 | 22.01.14 | |
| 11 | 22.01.14 | 28.01.14 | 29.01.14 | |
| 12 | 29.01.14 | 04.01.14 | 05.01.14 |
Material
Most documents are only locally accessible - see possibilities for remote access.Data
- Data can be downloaded here
Slides
- Stochastic actor-oriented models (slides) last updated: February 05, 2014.
- Stochastic actor-oriented models (slides) as handout (4 slides per page) last updated: February 05, 2014
- Static Network Models last updated: 02 December 2013.
Code examples / software
Literature
Lecture topics
- Batagelj, Brandes: Efficient Generation of Large Random Networks. Physical Review E 71, 036113, 2005.
- Lusher, Koskinen, and Robins: Exponential Random Graph Models for Social Networks. Cambridge Univ. Press, 2013.
- Robins, Pattison, Kalish, and Lusher: An introduction to exponential random graph (p*) models for social networks. Social Networks 29(2):173-191, 2007. (local copy)
- Snijders, van de Bunt, and Steglich: Introduction to stochastic actor-based models for network dynamics. Social Networks 32(1):44-60, 2010. (local copy)
- Snijders, Koskinen, and Schweinberger: Maximum Likelihood Estimation for Social Network Dynamics. Annals of Applied Statistics 4(2):567-588, 2010. (local copy)
- Steglich, Snijders, and Pearson: Dynamic Networks and Behavior: Separating Selection from Influence. Sociological Methodology 40(1):329-393, 2010. (local copy)
Background and further reading
- Lazer, Pentland, Adamic, Aral, Barabási, Brewer, Christakis, Contractor, Fowler, Gutmann, Jebara, King, Macy, Roy, Van Alstyne: Computational Social Science. Science 323(5915), 721-723, 2009.
- Marina Hennig, Ulrik Brandes, Jürgen Pfeffer and Ines Mergel: Studying Social Networks. Campus-Verlag, 2012.
- Brandes, Erlebach (Eds.): Network Analysis. LNCS 3418, Springer, 2005.
- Wasserman, Faust: Social Network Analysis. Cambridge Univ. Press, 1994.