Publication Date

Spring 2017

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Katerina Potika

Second Advisor

Sami Khuri

Third Advisor

Margareta Ackerman


social networks, community detection


The rise of the Internet has brought people closer. The number of interactions between people across the globe has gone substantially up due to social awareness, the advancements of the technology, and digital interaction. Social networking sites have built societies, communities virtually. Often these societies are displayed as a network of nodes depicting people and edges depicting relationships, links. This is a good and e cient way to store, model and represent systems which have a complex and rich information. Towards that goal we need to nd e ective, quick methods to analyze social networks. One of the possible solution is community detection. The community detection deals with nding clusters, groups in a network. Detecting such communities is very important in many elds in order to understand and extract the information from complex systems. The problem is very hard and has been studied extensively for the past few years. With this project, we will de ne the problem, study existing methods, propose new methods, and experimentally evaluate them using synthetic and real datasets. Additionally, we will describe applications to smart city communities and challenges that have to be resolved.