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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Interactive Graph Construction for Graph-Based Semi-Supervised Learning.

Changjian Chen, Zhaowei Wang, Jing Wu

    IEEE Transactions on Visualization and Computer Graphics
    |May 28, 2021
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    Summary
    This summary is machine-generated.

    This study introduces an interactive visual analysis method for constructing high-quality graphs in semi-supervised learning (SSL). The approach enhances prediction model performance by effectively utilizing both labeled and unlabeled data through improved graph construction.

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    Area of Science:

    • Machine Learning
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Semi-supervised learning (SSL) leverages unlabeled data to enhance prediction model performance.
    • Graph construction is crucial for SSL, but its quality directly impacts model efficacy.
    • Existing methods often lack effective strategies for interactive graph refinement.

    Purpose of the Study:

    • To present a visual analysis method for interactively constructing high-quality graphs in SSL.
    • To improve the performance of prediction models by optimizing graph structures.
    • To enable users to understand and modify graph properties for better SSL outcomes.

    Main Methods:

    • Proposes an interactive graph construction method based on the large margin principle.
    • Develops novel visualizations: a river visualization and a hybrid visualization (scatterplot, node-link, bar chart).
    • Facilitates user inspection and modification of graphs based on visualized label propagation.

    Main Results:

    • Demonstrates that high-quality graph construction significantly improves SSL model performance.
    • Case studies show the method's effectiveness in exploiting labeled and unlabeled samples.
    • The interactive approach allows for targeted graph refinement, leading to better predictions.

    Conclusions:

    • Interactive visual analysis is effective for building high-quality graphs in SSL.
    • The proposed method enhances the utility of unlabeled data for model improvement.
    • This approach offers a practical solution for optimizing graph-based SSL performance.