Linhares, Cláudio D.G. ORCID: https://orcid.org/0000-0001-7012-4461, Ponciano, Jean R., Pereira, Fabíola S., Rocha, Luís E.C. ORCID: https://orcid.org/0000-0001-9046-8739, Paiva, Jose Gustavo and Travençolo, Bruno A. ORCID: https://orcid.org/0000-0001-7690-301X (2019) A scalable node ordering strategy based on community structure for enhanced temporal network visualization. Computers and Graphics, 84. pp. 185-198. ISSN 0097-8493 (doi:10.1016/j.cag.2019.08.006)

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Abstract

Temporal networks have been used to map the structural evolution of social, technological, and biological systems, among others. Due to the large amount of information on real-world temporal networks, increasing attention has been given to issues related to the visual scalability of network visualization layouts. However, visual clutter due to edge overlap remains the main challenge calling for efficient methods to improve the visual experience. In this paper, we propose a novel and scalable node reordering approach for temporal network visualization, named Community-based Node Ordering (CNO), combining static community detection with node reordering techniques to enhance the identification of visual patterns. The perception of trends, periodicity, anomalies, and other temporal patterns, is facilitated, resulting in faster decision making. Our method helps not only the study of network activity patterns within communities but also the analysis of relatively large networks by breaking down its structure in smaller parts. Using CNO, we further propose a taxonomy to categorize activity patterns within communities. We performed a number of experiments and quantitative analyses using two real-world networks with distinct characteristics and showed that the proposed layout and taxonomy speed up the identification of patterns that would otherwise be difficult to see.

Item Type:	Article
Additional Information:	** Article version: AM ** Embargo end date: 31-12-9999 ** From Elsevier via Jisc Publications Router ** History: accepted 21-08-2019; issue date 26-08-2019. ** Licence for AM version of this article: This article is under embargo with an end date yet to be finalised.
Uncontrolled Keywords:	Special Section on SIBGRAPI 2019.
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HB Economic Theory T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Business
SWORD Depositor:	Users 6393 not found.
Last Modified:	13 Jun 2024 12:05
URI:	http://gala.gre.ac.uk/id/eprint/25038

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