Predicting Interactions and Information Diffusion in Social Networks

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Social network analysis is a wide field in which researchers focus on the creation of models which explain social phenomenons like user behaviour, the prediction or detection of trending topics in the world wide web, social media and many others environments. Since research is bound to the availability of relevant data sets which are used for evaluation, there are mainly two kinds of scenarios. As online content by its very nature can generally be obtained by crawling techniques or created by the maintainer of the website by querying the respective database oranalysing log-files, a lot of scenarios capture the state of online social networks or represent the linking structure for parts of the internet. The second source of data which is recently getting increasingly more attention with the growing availability of sensors in mobile phones is the digital representation of user (inter-) actions in the real world. Those latter scenarios are often attributed as offline. In this thesis, we answer questions in the field of link prediction and information diffusion and evaluate our solutions in form of models for online and offline scenarios. The first problem is defined as the task to predict which edges in a temporally evolving network will be present in the future based on the information in the past. Some frequently used evaluation settings are the linking structure between web sites, the mention-relation in Twitter, the friendship relation in social networks or the citation and co-author network of a scientific community. We focus on user interactions in online social networks. The second of both fields tackles the task to predict the spread of knowledge in a social network. Those models are often used in order to predict the potential of viral marketing strategies which explicitly consider the mechanisms of wordof-mouth. We evaluate our research in the context of online and offline social networks.