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    This study introduces SEAL-CI, a novel semi-supervised method for hierarchical graph node classification. It effectively classifies graph instances within a larger graph structure, even with limited labels.

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    Area of Science:

    • Graph Learning
    • Machine Learning
    • Network Science

    Background:

    • Node and graph classification are key graph learning tasks.
    • Nodes typically represent entities, but here, nodes are graph instances, creating a hierarchical graph structure.
    • This hierarchical perspective is relevant in social networks, biological networks, and document collections.

    Purpose of the Study:

    • To address node classification in hierarchical graphs where nodes are graph instances.
    • To develop a novel semi-supervised learning method for this challenging setting.
    • To introduce a method that handles limited labeled data effectively.

    Main Methods:

    • Proposed SEAL-CI, an iterative semi-supervised framework.
    • SEAL-CI updates graph instance-level and hierarchical graph-level modules.
    • Introduced Hierarchical Graph Mutual Information (HGMI) for inter-level consistency, with a guaranteed computation method.

    Main Results:

    • Demonstrated the effectiveness of the hierarchical graph modeling approach.
    • Validated the performance of the SEAL-CI method on real-world text and social network data.
    • Showcased successful node classification in a hierarchical graph setting.

    Conclusions:

    • Hierarchical graph modeling is a powerful approach for complex network structures.
    • SEAL-CI offers an effective semi-supervised solution for node classification in hierarchical graphs.
    • The proposed HGMI metric aids in enforcing consistency across different graph levels.