The State of the Art in Visualizing Dynamic Multivariate Networks

Author:

Kale Bharat12ORCID,Sun Maoyuan1ORCID,Papka Michael E.32ORCID

Affiliation:

1. Department of Computer Science Northern Illinois University USA

2. Argonne National Laboratory USA

3. Department of Computer Science University of Illinois Chicago USA

Abstract

AbstractMost real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.

Funder

Office of Science

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design

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