Stream-based Community Discovery via Relational Hypergraph Factorization on Evolving Networks

The discovery of communities or interrelations in social networks has become an important area of research. The increasing amount of information available in these networks and its decreasing life-time poses tight constraints on the information processing storage of the data is often prohibited due to its sheer volume. In this paper we adapt a exible approach for community discovery offering the integration of new information into the model. The continuous integration is combined with a time-based weighting of the data allowing for disposing obsolete information from the model building process. We demonstrate the usefulness of our approach by applying it on the popular Twitter network. The proposed solution can be directly fed with streaming data from Twitter, providing an up-to-date community model.