The course will start with a review of classic graph theory and game theory as well as a brief introduction to various techniques in data analysis, mining, machine learning, and social computing. It will then examine the data and techniques used to construct and analyze social networks, focusing on social networking websites, such as Facebook, twitter, etc. The course will also explore applications of social network analysis and mining in various domains, including health informatics, finance, security, etc.

Theoretical and practical knowledge about social network analysis and mining and its deployment in various application domains.

Consent of Organizers (a good working knowledge of programming is preferred. In addition, basic knowledge of linear algebra and probability & statistics will be useful as well).

The course is open for senior undergraduate students, graduate students, researchers, and practitioners.

Attending students will be eligible to earn five (5) credits (ECTS) from University of Southern Denmark. The summer course is also listed as a Ph.D. course in the Danish PhD Course database. Certificates will be distributed to all participants upon successful completion of the course.

Seats will be limited and eligible registrants will be admitted as their applications are received.

A minimal registration fee will be charged to cover expenses, including course material, food/beverage (lunch, breaks and dinner daily during the course).

There is no required text for the course. Relevant papers and material will be made available as necessary on the course webpage. The following books might be good general references:

1. Social Network Analysis: Methods and Applications by Stanley Wasserman and Katherine Faust, Cambridge University Press, 1994.
2. Social Network Analysis, by David Knoke, and Song Yang, Sage Publications.
3. The Structure and Dynamics of Networks, by Mark Newman, Albert-Laszlo Barabasi, Duncan J. Watts, Princeton University Press, 2006.
4. Six Degrees, by Duncan Watts, W.W. Norton & Co., 2004.
5. Models and Methods in Social Network Analysis, by Peter J. Carrington (Editor), John Scott (Editor), Stanley Wasserman (Editor).
6. Social Network Analysis and Mining Journal, by Reda Alhajj (Editor), ISSN: 1869-5469
7. Lecture Notes in Social Networks, by Reda Alhajj (Editor), Uwe Glässer (Editor), ISSN: 2190-5428
8. Social Media Mining: An Introduction by Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, Cambridge University Press, ISBN: 9781107018853. February, 2014.
9. Twitter Data Analytics, preprint for free download with code, by Shamanth Kumar, Fred Morstatter, and Huan Liu, Springer, December, 2013.
10. Provenance Data in Social Media, by Geoffrey Barbier, Zhuo Feng, Pritam Gundecha, and Huan Liu, Morgan & Claypool Publishers, May, 2013.
11. Evolution of Social Networks. Part III. Special double issue of Journal of Mathematical Sociology, by Patrick Doreian and Frans N. Stokman (eds), 2003, 27, 130 pp.
12. Exploratory Social Network Analysis with Pajek (Structural Analysis in the Social Sciences), Wouter De Nooy, Andrej Mrvar, Vladimir Batagelj, September 30, 2011
13. Techniques and Tools for Designing an Online Social Network Platform by Panagiotis Karampelas, Lecture Notes in Social Networks, Volume 3, Springer, 2013
14. Social Media Mining: An Introduction by Huan Liu, Cambridge University Press, 2014

Lectures, lab exercises, project.

Assessment of project and exercises, pass/fail, approval by the responsible instructor.

All students participating in the summer course are expected to have their own laptops or notebook to use during the tutorials and labs. Wi-Fi internet connection will be available in the area of the venue.

You are encouraged to download and install on your computer the following three software packages:

1. WEKA for data mining
2. Pajek for social network analysis
3. ORA for social network analysis

If you cannot manage to download these software packages then do not worry we will help you installing them during the summer course.