Enabling context-sensitive information seeking
Michelle X. Zhou, Keith Houck, et al.
IUI 2005
Dynamic network visualization has been a challenging research topic due to the visual and computational complexity introduced by the extra time dimension. Existing solutions are usually good for overview and presentation tasks, but not for the interactive analysis of a large dynamic network. We introduce in this paper a new approach which considers only the dynamic network central to a focus node, also known as the egocentric dynamic network. Our major contribution is a novel 1.5D visualization design which greatly reduces the visual complexity of the dynamic network without sacrificing the topological and temporal context central to the focus node. In our design, the egocentric dynamic network is presented in a single static view, supporting rich analysis through user interactions on both time and network. We propose a general framework for the 1.5D visualization approach, including the data processing pipeline, the visualization algorithm design, and customized interaction methods. Finally, we demonstrate the effectiveness of our approach on egocentric dynamic network analysis tasks, through case studies and a controlled user experiment comparing with three baseline dynamic network visualization methods.
Michelle X. Zhou, Keith Houck, et al.
IUI 2005
Chao Zhu, Chen Wang, et al.
SOLI 2010
Liangliang Cao, John Smith, et al.
WWW 2012
Michelle X. Zhou, Zhen Wen, et al.
IUI 2005