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Related Experiment Video

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Graph-Oriented Instruction Tuning of Large Language Models for Generic Graph Mining.

Yanchao Tan, Hang Lv, Pengxiang Zhan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 26, 2025
    PubMed
    Summary
    This summary is machine-generated.

    MuseGraph integrates Graph Neural Networks (GNNs) and Large Language Models (LLMs) for versatile graph mining. This foundation model enhances accuracy across diverse graph tasks and datasets without task-specific retraining.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Graph Neural Networks (GNNs) traditionally require task-specific retraining.
    • Large Language Models (LLMs) show promise but are underexplored for generic graph mining.
    • A unified model for diverse graph tasks and datasets is needed.

    Purpose of the Study:

    • To develop a novel framework, MuseGraph, integrating GNNs and LLMs for versatile graph mining.
    • To enable a single model to handle multiple graph tasks and datasets simultaneously.
    • To enhance LLMs' generative abilities while improving graph mining performance.

    Main Methods:

    • Developed a compact graph description for language token efficiency.
    • Proposed a diverse instruction generation mechanism with Chain-of-Thought (CoT) for distilling LLM reasoning.
    • Designed a graph-aware instruction tuning strategy to prevent catastrophic forgetting and promote mutual enhancement.

    Main Results:

    • MuseGraph demonstrated significant improvements across five graph tasks and ten datasets.
    • The framework enhances accuracy in graph-oriented downstream tasks.
    • Improved LLM generation abilities were observed alongside graph mining performance.

    Conclusions:

    • MuseGraph offers a powerful foundation model for generic graph mining.
    • The integration of GNNs and LLMs presents a promising direction for future research.
    • This approach addresses limitations of traditional GNNs and expands LLM applications.