H. Liu, P. Eades, and S. Hong: Visualizing Dynamic Trajectories in Social Networks. VL/HCC 2012, accepted.
Dynamic Social network visualization transforms dynamic information in a social network into geometric representations. Most previous work in this field mainly focuses on the evolution of the overall network.
In this paper, we present a new framework for edge bundling, which tightly In this paper, we present a new framework to visualize dynamic individual trajectories in a social network. In particular, we introduce two visual models: concept map and temporal representation. We present three methods to visualize dynamic trajectories: animation, color representation and line representation.
The results of our case studies with academic collaboration networks and sports networks indicate that our new framework can be very useful for visual analysis of social networks.