Interactive temporal display through collaboration networks visualization

Visual analytics play an important role in understanding complex datasets. The bibliographic database is often visualized as a collaboration network to illustrate the connections between researchers. Static networks, however, barely reveal any information when the dataset includes temporal variables. In this article, we propose an embedded network visualization to display the temporal patterns hiding in the data and use intelligent filters to avoid occlusion. We examined different graphing styles, such as the temporal display and the time direction, to find the best way to present the temporal features. In addition, we demonstrate the utility of our approach with case studies and evaluations of real bibliographic databases