A method for characterizing communities in dynamic attributed complex networks

Many methods have been proposed to detect communities in complex networks, but very little work has been done regarding their interpretation. In this work, we propose an efficient method to tackle this problem. We first define a sequence-based representation of networks, combining temporal information, topological measures and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset and use them to characterize the communities. We also show how to highlight outliers. Finally, as an illustration, we apply our method to a network of scientific collaborations.