The State of the Art in Visualizing Dynamic Multivariate Networks

Abstract

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.

ProtEGOnist: Visual Analysis of Interactions in Small World Networks Using Ego‐graphs

“…Ego-graph visualizations are extensively used in the domain of dynamic graphs, as shown in a recent review by Kale et al [KSP23]. While dynamic graphs represent a data structure with specific tasks and use cases, some of the visualization concepts apply to static graphs.…”

State of the Art of Graph Visualization in non‐Euclidean Spaces

“…We model our methodology on similar surveys. Kale et al review dynamic multivariate networks [KSP23] and use a similar multidimensional tagging system to our methodology. A related survey categorizes works in geospatial network visualization [SYPB21].…”