Network Science is an interdisciplinary discipline focusing on the study of complex networks, i.e., the structure of the connections between entities, for example social relationships between individuals. The application fields of Network Science are numerous and span several sub-areas depending on the nature of the connected entities, including not only social networks but also biological, communication and computer networks. The main objective of the field is to develop predictive models for physical, social and biological phenomena.
Most of the course will focus on online social networks as an application context, but many concepts (including analysis measures and data mining) are generally applicable to any system that can be modeled as a network.
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 (Barabasi & Albert, 1999). In the following years, popular science books (Barabasi, 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.
(Barabasi, 2002) A. L. Barabasi. Linked: The New Science of Networks. Perseus Books Group.
(Barabasi & 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).
Lecture 1: April 22, 10:00-12:00
Networks: an introduction
Lecture 2: April 29, 10:00-12:00
Models and measures (1)
Lecture 3: April 30, 10:00-12:00
Models and measures (2)
Lecture 4: May 6, 10:00-12:00
Network Mining (community detection)
Lecture 5: May 8, 10:00-12:00
Information propagation
Lecture 6: May 13, 10:00-12:00
Linguistic processing of social media (1)
Lecture 7: May 15, 10:00-12:00
Linguistic processing of social media (2)
Lecture 8: May 20, 10:00-12:00
Network formation and social models (to be confirmed)
Lecture 9: May 22, 10:00-12:00
Security and privacy in online social networks
May 26, 27 and 28, 9:00-12:00: Student presentations (to be updated depending on number of subscribers)
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 3 credits.
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 materials where needed.