PhD course on Network Science - Fall 2016

Uppsala University

Head teachers

Matteo Magnani, PhD
(Uppsala University, IT department)
Christian Rohner, PhD
(Uppsala University, IT department)

Guest lecturers

Vladimir Batagelj, Institute of Mathematics, Physics and Mechanics, Ljubljana, Slovenia
Leon Derczynski, University of Sheffield, UK
Lars Juhl Jensen, University of Copenhagen, Denmark
Merkouris Karaliopoulos, Athens University of Economics and Business, Greece (TBC)
Márton Karsai, ENS de Lyon, France
Francesca Pallotti, Centre for Business Network Analysis, University of Greenwich


Network Science is an interdisciplinary field aimed at developing predictive models for physical, social and biological phenomena that can be modeled as sets of interconnected entities.

This discipline is fairly recent, even though it incorporates several consolidated areas of knowledge such as graph theory (dating back to the Eighteenth century), social network analysis (originated in the first half of the Twentieth century) and Data Mining. In particular, it became popular among researchers thanks to several break-through works appeared in major scientific journals, including Watts and Strogatz's small world model (Watts & Strogatz, 1998), providing an explanation of the so called six degrees of separation hypothesis, and Barabasi and Albert's scale-free networks (Barabási & Albert, 1999). In the following years, popular science books (Barabási, 2002), press coverage and significant research fundings (e.g., from the U.S. Department of Defense) contributed to the development of a field that is now entering its maturity, but still provides a large number of challenging open problems.

(Barabási, 2002) A. L. Barabasi. Linked: The New Science of Networks. Perseus Books Group.
(Barabási & Albert, 1999) A. L. Barabási and R. Albert. Emergence of scaling in random networks. Science, 286 (5439).
(Watts & Strogatz, 1998) D. J. Watts and S. H. Strogatz. Collective dynamics of small-world networks. Nature, 393 (6684).

Tentative schedule

The course consists of four occasions, each covering two full days:

November 3/4 - Networks: an introduction
Thu, Nov. 3, 09:15-10:00, Å72121: Introduction to the course
Thu, Nov. 3, 10:15-12:00, Å72121: Network models and measures (part I)
Thu, Nov. 3, 13:15-16:00, 1211, ITC: Network models and measures (part II)

Fri, Nov. 4, 09:15-12:00, 1213, ITC: Graph mining (community detection)
Fri, Nov. 4, 13:15-16:00, 1113, ITC: Propagation

November 17/18 - Focused invited talks I
Thu, Nov. 17, 09:15-12:00, 2244, ITC - Multilayer networks (Magnani)
Thu, Nov. 17, 13:15-15:00, 2244, ITC - Protein networks (Jensen)
Fri, Nov. 18, 09:15-11:00, 2214, ITC - Temporal Networks (Karsai)
Fri, Nov. 18, 11:15-12:00, 2214, ITC - Bibliographic networks I (Batagelj)
Fri, Nov. 18, 13:15-15:00, 2345, ITC - Bibliographic networks II (Batagelj)

December 1/2 - Focused invited talks II
Thu, Dec. 1, 09:15-12:00, 2347, ITC - Language and Networks (Derczynski)
Thu, Dec. 1, 13:15-15:00, 2345, ITC - TBC (Karaliopoulos)
Fri, Dec. 2, 09:15-12:00, 1212, ITC - ERGMs of Organizational Networks (Pallotti)
Fri, Dec. 2, 13:15-15:00, 1212, ITC - TBC

December 15/16 - Student seminars
Time and location TBC

Credits and assessment

Students will either prepare a short seminar on state of the art research on one selected topic of interest, for which the teachers will provide references to relevant papers, or prepare and discuss a project proposal involving the application of some methods covered during the course to their research area. Grading will be on the U/G scale, and the course corresponds to 5 credits.

Expected level and prerequisites

The course is targeted to PhD students willing to apply network science in their own discipline, but also experienced researchers with a consolidated research background in different areas, willing to explore potential interdisciplinary research directions. Being an interdisciplinary course intended for a broad audience, the topics will be presented in a self-contained way, giving pointers to more advanced material where needed.


The course is free for all PhD students. If interested, please send an email to specifying your name, department, research field and one sentence motivating your interest.