Visualizing the impact of time series data for predicting user interactions

In recent years the importance of user interactions has been recognized in a variety of research contexts. There is a variety of algorithms for modeling these in social graphs; in particular, we distinguish static and dynamic relations. In contrast to static graphs in which the networks do not change over time, the underlying relation is changing frequently in various contexts. This should be reflected by a time dependent social neighborhood of users. In this paper, we present a new and intuitive visualization concept for the histories of user interactions. We derive association rules and visualize these using heatmaps. We demonstrate the impact of the presented approach by several examples utilizing real-world data -- using the well known twitter dump of 2009.