Learning goal is to be able to recognize and explain network phenomena. Social networks such as Facebook, information networks such as the Web, and institutions such as voting are all IT-enabled. The student will learn:
After following this module, the student is able to:
- how to recognize and explain structural and dynamic phenomena in these networks, such as cascading behavior and power laws, and
- how to model and analyze using graph theory and game theory.
In this study unit of the module Web Science we focus on one specific topic, in order to deepen practical knowledge and skills regarding this topic.
- Recognize these phenomena in practice;
- Apply mathematical models from graph theory, probability, and game theory to describe and analyze them;
- Explain and predict network phenomena in terms of network structure and behavior;
- Operationalize and apply these models to existing network data.
Students will work in small groups on an assignment related to one of the following topics: graphs and social networks, information networks, network dynamics, game theory and network traffic, auctions and matching markets, and institutions and aggregate behaviour. There are several assignments to choose from, but each assignment can only be chosen by a limited number of groups.|
Each assignment requires to study a topic from the book in more depth, typically using supplementary material that is provided by the teachers or has to be acquired by a literature search. The supplementary material could include data sets that have to be analyzed.
The results of the study have to be reported in an essay, and orally presented to the teachers and the peer groups.