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    This study introduces GraphQ, a visual analytics system for subgraph pattern search in graph databases. It uses graph neural networks (GNNs) and a novel node-alignment method, NeuroAlign, for accurate and efficient pattern identification.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Machine Learning

    Background:

    • Graphs are essential data structures for modeling complex relationships across various domains.
    • Subgraph pattern identification is key to understanding graph structures.
    • Existing methods struggle with accurate node correspondence in subgraph matching.

    Purpose of the Study:

    • To develop a visual analytics system (GraphQ) for example-based subgraph pattern search in large graph databases.
    • To introduce NeuroAlign, a novel graph neural network (GNN) for accurate node alignment in subgraph matching.
    • To enhance the interpretability and validation of subgraph search results.

    Main Methods:

    • Utilized graph neural networks (GNNs) for encoding graphs into latent vector representations for efficient querying.
    • Developed a novel GNN, NeuroAlign, specifically for precise node-to-node correspondence in subgraph matching.
    • Integrated a visual query interface, multi-scale result visualization, and user feedback mechanisms within GraphQ.

    Main Results:

    • NeuroAlign demonstrated a 19%-29% improvement in node-alignment accuracy over baseline GNNs.
    • Achieved up to a 100x speedup in subgraph matching compared to traditional combinatorial algorithms.
    • Qualitative studies confirmed GraphQ's effectiveness in program analysis and semantic scene graph search.

    Conclusions:

    • GraphQ, powered by NeuroAlign, offers an effective and efficient solution for subgraph pattern search and analysis.
    • The system facilitates human-in-the-loop exploration and refinement of complex graph data.
    • The proposed methods significantly advance the accuracy and speed of subgraph matching and interpretation.