PROJECT TITLE :
1.5D Egocentric Dynamic Network Visualization
Dynamic network visualization has been a difficult analysis topic thanks to the visual and computational complexity introduced by the extra time dimension. Existing solutions are typically smart for overview and presentation tasks, however not for the interactive analysis of a large dynamic network. We introduce in this paper a new approach which considers solely the dynamic network central to attention node, also known as the egocentric dynamic network. Our major contribution may be a novel one.5D visualization style which greatly reduces the visual complexity of the dynamic network while not sacrificing the topological and temporal context central to the main target node. In our design, the egocentric dynamic network is presented in a very single static read, supporting made analysis through user interactions on each time and network. We tend to propose a general framework for the 1.5D visualization approach, together with the data processing pipeline, the visualization algorithm style, and customised interaction strategies. Finally, we have a tendency to 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 strategies.
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