DMNEVis: A Novel Visual Approach to Explore Evolution of Dynamic Multivariate Network
Di Peng, Wei Dong Tian, Binbin Lu, Min Zhu · 2018
The multivariate network consists of a series of nodes and links with multiple attributes. The topology and multivariate information of network will change over time, namely with dynamic change. Many real-world physical and non-physical phenomena can be modeled as such networks, such as population migration, proteins interactions, transactions, etc. It is of great application value for different domains if users can effectively mine the potential information in the process of networks evolution. However, existing visual analytics systems of multivariate network focus on group network or ego-centric network respectively, and fail to analyze the evolution of both them. To solve this problem, we propose DMNEVis (Dynamic Multivariate Network Evolution Visualization), a visual analytics system that helps explore the evolution of the dynamic multivariate network from group network to ego-centric network step by step. The system provides a series of novel visual tools for users to understand evolution from both group and individual level. Finally, we demonstrate effectiveness of DMNEVis through a case study on the co-authorship dataset.