The State of the Art in Visualizing Dynamic Multivariate Networks

Kale, Bharat; Sun, Maoyuan; Papka, Michael E.

doi:10.1111/cgf.14856

Title: The State of the Art in Visualizing Dynamic Multivariate Networks

Journal Article · 26 June 2023 · Computer Graphics Forum

DOI: https://doi.org/10.1111/cgf.14856 · OSTI ID:1987432

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Department of Computer Science Northern Illinois University USA, Argonne National Laboratory USA
Department of Computer Science Northern Illinois University USA
Department of Computer Science University of Illinois Chicago USA, Argonne National Laboratory USA

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1987432

Journal Information:: Computer Graphics Forum, Journal Name: Computer Graphics Forum Journal Issue: 3 Vol. 42; ISSN 0167-7055

Publisher:: Wiley-BlackwellCopyright Statement

Country of Publication:: Netherlands

Language:: English

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