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TeKo: Text-Rich Graph Neural Networks With External Knowledge.

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    |June 14, 2023
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    This study introduces TeKo, a novel graph neural network (GNN) that integrates external knowledge to enhance analysis of text-rich networks by leveraging both structure and semantics.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Network Science

    Background:

    • Graph neural networks (GNNs) excel at analyzing graph data but often overlook textual information.
    • Existing methods for text-rich networks struggle to fully integrate and leverage semantic content.
    • This limits the synergistic relationship between network structure and textual data.

    Purpose of the Study:

    • To develop a novel GNN approach (TeKo) that effectively combines structural and textual information in text-rich networks.
    • To address the limitations of current methods in comprehensively mining textual semantics.
    • To enable reciprocal guidance between network structure and textual semantics for improved representation learning.

    Main Methods:

    • Proposed a flexible heterogeneous semantic network integrating documents and entities.
    • Incorporated external knowledge, including structured triplets and unstructured entity descriptions.
    • Devised a reciprocal convolutional mechanism for collaborative enhancement of structure and semantics.

    Main Results:

    • TeKo achieved state-of-the-art performance on various text-rich network benchmarks.
    • Demonstrated superior ability in leveraging both structural and textual information.
    • Successfully improved the learning of high-level network representations.

    Conclusions:

    • The proposed TeKo model offers a significant advancement in analyzing text-rich networks.
    • Integrating external knowledge enhances the understanding of textual semantics within networks.
    • TeKo provides a powerful framework for future research in graph representation learning.