IEEE VIS 2024 Content: On Network Structural and Temporal Encodings: A Space and Time Odyssey

On Network Structural and Temporal Encodings: A Space and Time Odyssey

Velitchko Filipov -

Alessio Arleo -

Markus Bögl -

Silvia Miksch -

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DOI: 10.1109/TVCG.2023.3310019

Room: Bayshore I

2024-10-16T18:21:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T18:21:00Z
Exemplar figure, described by caption below
This study evaluates the effectiveness of various network structural and temporal encodings in dynamic network visualization, focusing on Node-Link diagrams and Adjacency Matrices. Through two comprehensive studies, we assessed the accuracy, response times, and user preferences for different visualization techniques, including Juxtaposition, Superimposition, Auto-Animation, and Animation with Playback Controls. Our findings highlight the strengths and limitations of each approach, providing critical insights for optimizing dynamic network analysis and designing with tasks in mind. The figure illustrates key methods: Network structural and temporal encodings—Juxtaposition (A,D), Superimposition (B,E), and Animation with Playback Controls (C,F).
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Abstract

The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. Most studies explore the temporal dimension and diverse interaction techniques supporting the participants, focusing on a single structural representation. Empirical evidence about performance and preference for different visualization approaches is scattered over different studies, experimental settings, and tasks. This paper aims to comprehensively investigate the dynamic network visualization design space in two evaluations. First, a controlled study assessing participants' response times, accuracy, and preferences for different combinations of network structural and temporal representations on typical dynamic network exploration tasks, with and without the support of standard interaction methods. Second, the best-performing combinations from the first study are enhanced based on participants' feedback and evaluated in a heuristic-based qualitative study with visualization experts on a real-world network. Our results highlight node-link with animation and playback controls as the best-performing combination and the most preferred based on ratings. Matrices achieve similar performance to node-link in the first study but have considerably lower scores in our second evaluation. Similarly, juxtaposition exhibits evident scalability issues in more realistic analysis contexts.