Most 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.
Kale, Bharat, Sun, Maoyuan, & Papka, Michael E. (2022). The State of the Art in Visualizing Dynamic Multivariate Networks. https://doi.org/10.1111/cgf.14856
Kale, Bharat, Sun, Maoyuan, and Papka, Michael E., "The State of the Art in Visualizing Dynamic Multivariate Networks," (2022), https://doi.org/10.1111/cgf.14856
@conference{osti_2426395,
author = {Kale, Bharat and Sun, Maoyuan and Papka, Michael E.},
title = {The State of the Art in Visualizing Dynamic Multivariate Networks},
annote = {Most 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.},
doi = {10.1111/cgf.14856},
url = {https://www.osti.gov/biblio/2426395},
place = {United States},
organization = {Argonne National Laboratory (ANL)},
year = {2022},
month = {12}}
Widanagamaachchi, W.; Christensen, C.; Bremer, P. -T
2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV 2012), IEEE Symposium on Large Data Analysis and Visualization (LDAV)https://doi.org/10.1109/LDAV.2012.6378962