Visualization of Evolving Social Networks using Actor-Level and Community-Level Trajectories

In recent years we witnessed an impressive advance in the social networks field, which became a "hot" topic and a focus of considerable attention. Also, the development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. In this paper we present an approach to visualize the evolution of dynamic social networks by using Tucker decomposition and the concept of temporal trajectory. Our visualization strategy is based on the definition of trajectories, both at the node-level and at the community-level, in a bidimensional space that preserves its structural properties. Furthermore, this approach can be used to identify similar actors, or communities of actors, by comparing the shape and position of trajectories. To illustrate the proposed approach we conduct a case study using a set of temporal friendship networks.