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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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Explaining Deep Graph Networks via Input Perturbation.

Davide Bacciu, Danilo Numeroso

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    This study introduces a new explainability framework for deep graph networks (DGNs) using reinforcement learning. The method generates local graph explanations, enhancing trust in DGN applications like drug discovery and social networks.

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    Area of Science:

    • Machine Learning
    • Graph Neural Networks
    • Explainable AI

    Background:

    • Deep graph networks (DGNs) are increasingly used in life sciences and social networks.
    • Explaining DGN predictions is crucial due to privacy and safety concerns.
    • Existing explainability methods struggle with complex graph structures.

    Purpose of the Study:

    • To develop a novel local explanation framework for DGNs tailored to graph data.
    • To address the challenges posed by the combinatorial nature of graph structures in explainability.
    • To provide interpretable insights into DGN predictions for critical applications.

    Main Methods:

    • Leveraging reinforcement learning to generate meaningful local perturbations of input graphs.
    • Optimizing a multi-objective score considering structural and output similarities.
    • Fitting an interpretable model to local DGN behavior using generated neighboring samples.

    Main Results:

    • Demonstrated effectiveness through qualitative analysis on chemistry datasets (TOX21, ESOL).
    • Achieved quantitative results on a benchmark explanation dataset (CYCLIQ).
    • Successfully generated informative neighboring samples for local DGN interpretation.

    Conclusions:

    • The proposed framework offers effective local explanations for DGNs.
    • The approach is suitable for privacy and safety-critical domains like drug repurposing.
    • This work advances explainable AI for complex graph-structured data.