In recent years more and more social network platforms have been established. Some prominent examples are Facebook, Xing and LinkedIn. The platform providers collect a huge amount of data for each of their users. Besides personal information such as age, hobbies or professional career, also relations between users are specified. Consequently, each platform can be represented as a network of users where each user can be characterized with specific properties. The links between users may have different meanings. A very common meaning is that two users are connected if they know each other. But also other types of relations are possible: For example in interaction networks a connection between users is triggered by a communication between these users e.g. via e-mail or phone.
The data in social networks could contain valuable information. The research area called Social Network Analysis provides methods to gain these information. To achieve this purpose for example statistical methods and graph clustering algorithms such as EBC-clustering  were proposed. A recommendable introduction in the area of social network analysis is provided by Brandes and Erlebach.