Dynamic Multivariate Network Visualization Techniques: A STAR Report

An interactive filtering tool to suppliment the STAR report on dynamic multivariate network visualization techniques
Bharat Kale1, Maoyuan Sun1, Michael E. Papka2
1. Northern Illinois University
2. University of Illinois Chicago

This acts as a companion to our State-of-the-Art report on dynamic multivariate network visualization techniques and provides an interactive filtering tool to browse the categories in our taxonomy.

Most real-world networks are both dynamic and multivariate in nature. It means the networks have not only nodes, edges, and associated attributes but also they all evolve with time. We refer to such networks as Dynamic Multivariate Networks (DMVNs). For example, in a social network understanding the dynamics in relationships between people along with the dynamics in their characterstics such as their preferences is equally important.

Visualizing dynamic multivariate networks is of great significance to the information visualization community because of their wide applications across multiple scientific domains. Hence the comuunity has developed many techniques to visualize them and interact with them for performing various exploratory and analysis tasks. We propose a new taxonomy to classify these existing techniques and this website helps in interactively exploring the techniques at the intersections of our categories.

For any questions or comments, contact the authors.

Overview of our proposed taxonomy and the categories. Refer to the Dimensions section for more details.

Proposed Taxonomy