Interactive temporal display through collaboration networks visualization

Abstract

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.

A framework for social media information flow analytics in cyberspace and physical space

“…Ways of demonstrating nodes’ locations include a circle with uniform or varied size (Koylu, Delil, Guo, & Celik, 2018), a boundary of administrative units (Stephen & Jenny, 2017), and an outline of aggregated regions (Guo & Zhu, 2014; Zhu & Guo, 2014). Ways of showing flow direction include arrows (Guo, 2009; Koylu & Guo, 2017), tapered lines (Guo & Zhu, 2014; Koylu & Guo, 2017), clockwise (i.e., left‐hand traffic rule) or counter‐clockwise (i.e., right‐hand traffic rule) line orientation (Jing, Li, & Zhang, 2019; Koylu & Guo, 2017), and animated lines (Han et al., 2017). Ways of depicting flow volume include line width (Jenny et al., 2017), line color (Guo & Zhu, 2014), and arrow size (Jenny et al., 2018).…”