
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 ITenabled. The student will learn:
 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.
After following this module, the student is able to:
 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.
In this study unit of the module Web Science we focus on the topics: graphs and social networks, information networks, and network dynamics.


The study unit Social Network Structure and Dynamics of the module Web Science covers the following topics:
 Graphs and Social Networks
We study basic graph theory concepts such as components, triadic closure, strong and weak ties, homophily (similarity between 'friends') and positive and negative relationships. These concepts are put to work on modeling network data such as collaborations, information linkage, citation, interactions, etc. Students will be able to understand and model network data as graphs, and develop algorithms for analyzing basic graph properties of large volumes of network data (big data).
Our goal is to understand the structure of information networks on the internet that emerges from citation, liking, commenting, coauthoring, connection with 'friends', hypertext linking, etc. We study properties such as reputation, authority and relevance of web pages and persons. Students will learn to model, understand, and analyze such informational properties in terms of graph theory concepts.
 Network Dynamics – Population Models and Structural Models
We study how people connected in a network influence each other’s behaviour and decisions. First we consider population models which help us to understand informational (or herding) effects and directbenefit (or network) effects in social processes, and apply this knowledge to analyze the notion of popularity. Then we consider structural models to understand diffusion of information through groups of people, as opposed to a homogeneous population, and explain the small world phenomenon. Students will learn how to model and analyze the processes by which new ideas and innovations are adopted by a population in which groups of people are connected by very short paths.




 Assumed previous knowledgeSome experience with programming, specifically Python. 
Bachelor Technical Computer Science 
  Required materialsBookDavid Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World. ISBN: 9780521195331.
The book can be downloaded from http://www.cs.cornell.edu/home/kleinber/networksbook/. 

 Recommended materialsInstructional modesLecture
 Project unsupervised
 Q&A
 Self study without assistance
 Tutorial

 TestsTheoretical Knowledge
 Practical Knowledge and Skills


 