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MoNetExplorer: A Visual Analytics System for Analyzing Dynamic Networks With Temporal Network Motifs.

Seokweon Jung, DongHwa Shin, Hyeon Jeon

IEEE Transactions on Visualization and Computer Graphics

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MoNetExplorer aids dynamic network analysis by recommending optimal time window sizes for network snapshots. This visual analytics system helps researchers understand evolving network structures more efficiently.

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Area of Science:

Network Science
Data Visualization
Computer Science

Background:

Analyzing dynamic networks involves partitioning them into time-based snapshots.
Selecting appropriate time windows for these snapshots is a significant challenge, often requiring manual trial-and-error.
Understanding evolving network structures is crucial in various scientific domains.

Purpose of the Study:

To introduce MoNetExplorer, an interactive visual analytics system for dynamic network analysis.
To address the challenge of selecting optimal time window sizes for network partitioning.
To leverage temporal network motifs for recommending window sizes and comparing slicing results.

Main Methods:

Developed MoNetExplorer, an interactive visual analytics system.
Incorporated temporal network motifs to guide window size selection.
Provided a temporal overview, motif composition analysis, and node-link diagram details for different temporal resolutions.
Conducted case studies with network researchers using real-world dynamic network datasets.

Main Results:

MoNetExplorer effectively recommends window sizes for dynamic network partitioning.
The system facilitates visual comparison of different network slicing results.
Users can gain insights into temporal and structural patterns at various resolutions.
Case studies demonstrated the system's effectiveness in supporting user understanding.

Conclusions:

MoNetExplorer offers a novel and effective approach to analyzing dynamic networks.
The system reduces the trial-and-error process in selecting time windows.
It enhances the ability of researchers to explore and understand temporal network evolution.