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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Multicolor Time-lapse Imaging of Transgenic Zebrafish: Visualizing Retinal Stem Cells Activated by Targeted Neuronal Cell Ablation
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ZigzagNetVis: Suggesting Temporal Resolutions for Graph Visualization Using Zigzag Persistence.

Raphael Tinarrage, Jean R Ponciano, Claudio D G Linhares

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    ZigzagNetVis suggests optimal temporal resolutions for analyzing temporal graphs by using zigzag persistent homology. This method aids in uncovering hidden patterns in complex systems through improved visualization techniques.

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    Area of Science:

    • Graph theory
    • Topological Data Analysis (TDA)
    • Information Visualization

    Background:

    • Temporal graphs model dynamic systems, but analysis is hindered by challenges in selecting appropriate temporal resolution.
    • Visually analyzing temporal graphs is key for identifying trends and anomalies, yet choosing the right resolution, especially for sparse data, remains difficult.
    • Timeslicing, grouping events over time, can reveal patterns but requires careful selection of temporal resolution.

    Purpose of the Study:

    • To propose ZigzagNetVis, a novel methodology for suggesting relevant temporal resolutions for temporal graph analysis.
    • To enhance the visual analysis of temporal graphs by introducing a new timeline-based visualization technique.
    • To develop and evaluate a web-based system prototype implementing the proposed methodology and visualization tools.

    Main Methods:

    • Leveraging zigzag persistent homology, a technique from Topological Data Analysis (TDA), to identify resolutions causing significant topological changes.
    • Incorporating the colored barcode, a novel visualization inspired by persistence barcodes, for timeline-based graph analysis.
    • Developing a web-based system prototype to demonstrate the ZigzagNetVis methodology and its visualization capabilities.

    Main Results:

    • ZigzagNetVis effectively suggests temporal resolutions that highlight substantial topological shifts in graph structures.
    • The colored barcode visualization provides an intuitive way to explore temporal graph patterns.
    • A user study with 27 participants and quantitative evaluations confirmed the usefulness and effectiveness of ZigzagNetVis.

    Conclusions:

    • ZigzagNetVis offers a robust approach to determining optimal temporal resolutions for analyzing temporal graphs.
    • The proposed methodology and visualization tools significantly improve the process of visual graph analysis, particularly for sparse temporal data.
    • The developed system prototype demonstrates practical applicability and validates the effectiveness of ZigzagNetVis in real-world scenarios.