Staged Animation Strategies for Online Dynamic Networks

Crnovrsanin, Tarik; Shilpika,; Chandrasegaran, Senthil; Ma, Kwan‐Liu

doi:10.1109/tvcg.2020.3030385

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…Among the animated layouts, the node-link diagram is still one of the most preferred representations [12]. It is used, e.g., to visualize online networks, where nodes and edges arrive in a real-time manner [19]. While it can suffer from scalability limitations when applied to networks with several nodes and edges per timestamp, representation simplification (e.g., super nodes [63]) may attenuate this problem.…”

Section: Visualization Of Temporal Networkmentioning

confidence: 99%

LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Linhares¹,

Ponciano

Pedro³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. LargeNetVis is a web-based visual analytics system designed to support the visual exploration of temporal networks with up to a few thousand nodes and timestamps. The system is composed of four linked visual components. While the Taxonomy Matrix (A) and Global View (B) enable global-level analysis, the node-link diagram (C) and the Temporal Activity Map (D) enable structural and temporal local-level analysis, respectively. The system also provides a panel with the network, community, or node numerical information (Community detail (E)). A tooltip with extra information is shown when a community is hovered over in Global View (F).

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Section: Visualization Of Temporal Networkmentioning

confidence: 99%

LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Linhares¹,

Ponciano

Pedro³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…With the increasing scale of dynamic graph data, traditional sequential node-link and matrix-based diagrams fail to display all the graphs in a plane which is space limited (e.g., computer and mobile screen, etc). To address above problems, researches design many techniques to visualizing dynamic graphs such as graph animation [1,11,32], parallel-based node-link diagram [5,14], graph snapshots [6,43], graph navigation [19,25], graph projection [13], set-based graph [34], graph clustering [17,47], and hypergraph visualization [29,41]. For example, animating the graphs according to the timestamps in dynamic graph evolution analysis and structure analysis can reduce the showing space, transferring the time for space [1].…”

Section: Dynamic Graph Visualizationmentioning

confidence: 99%

“…In recent years, integrating visual analytic methods becomes a popular selection for researchers in analyzing dynamic graphs. Many dynamic graph visualization works focus on animating graph topology [11,32], encoding graph elements in effective visual manners [2,54], providing overview of timestamps [6,18], comparing the topology structures [19], etc. However, current dynamic graph visualization works still have a gap in helping users analyze the large-scale and time-intensive dynamic graph data with subtle changes such as basketball player networks in a competition.…”

Section: Introductionmentioning

confidence: 99%

DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph

Chang¹

2022

Preprint

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s1 d average player speed average link distance average link stabiliity Fig. 1. The system interface of DGSVis includes (a) a matrix to provide an overview of basketball player networks, (b) a scatter plot to offer a projection based on the vector of basketball player networks, (c) a mix of the line chart and node-link diagram to integrate a additional event information, (d) a tree diagram to present a snapshot generation operation flow, (e) a node-link diagram to display the details of snapshots on the field, and (f) combined line charts to show players' detailed attributes.

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“…To convey complex changes, animations adopt staging techniques that divide the given changes into multiple sub-sequences and animate them consecutively. Staged animations have outperformed unstaged variants for value estimation and change tracking [11], conveying aggregate operations [14], and understanding online dynamic networks [5]. Gemini 2 aims to aid the authoring of staged animations by generating them automatically from given keyframes and by recommending effective keyframes for given start and end visualization states.…”

Section: Gemini Recommendationmentioning

confidence: 99%

Gemini2: Generating Keyframe-Oriented Animated Transitions Between Statistical Graphics

Kim

Heer

2021

Preprint

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Complex animated transitions may be easier to understand when divided into separate, consecutive stages. However, effective staging requires careful attention to both animation semantics and timing parameters. We present Gemini 2 , a system for creating staged animations from a sequence of chart keyframes. Given only a start state and an end state, Gemini 2 can automatically recommend intermediate keyframes for designers to consider. The Gemini 2 recommendation engine leverages Gemini, our prior work, and GraphScape to itemize the given complex change into semantic edit operations and to recombine operations into stages with a guided order for clearly conveying the semantics. To evaluate Gemini 2 's recommendations, we conducted a human-subject study in which participants ranked recommended animations from both Gemini 2 and Gemini. We find that Gemini 2 's animation recommendation ranking is well aligned with subjects' preferences, and Gemini 2 can recommend favorable animations that Gemini cannot support.

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Staged Animation Strategies for Online Dynamic Networks

Cited by 10 publications

References 34 publications

LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph

Gemini2: Generating Keyframe-Oriented Animated Transitions Between Statistical Graphics

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