The right hand side image depicts a small area in a social network. The nodes represent the users and the edges the friendship relation between users. Different colours model different communities.
Social networks are omnipresent in our daily life. Facebook has as much as 1.4 billion daily active users and on Twitter around 500 million tweets are posted every day. Messages spreading on social networks have a significant influence on our social and economic behaviour. One of the main goals of this use case is to understand the spread of messages as well as the influence of social networks. As a second goal, we plan to identify false/malicious messages, which intend to change the behaviour of a substancial number of users. Additionally, we aim to develop countermeasures on the algorithmic level in order to prevent the spread of such false messages on a large scale. Finally, we will develop a highly scalable simulation framework for such stochastic processes in real-world networks, in order to be able to analyse and predict the impact of these processes on the society.