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HeGCL: Advance Self-Supervised Learning in Heterogeneous Graph-Level Representation.

Gen Shi, Yifan Zhu, Jian K Liu

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    Summary
    This summary is machine-generated.

    This study introduces HeGCL, a novel self-supervised framework for heterogeneous graphs. It effectively captures global graph properties and enhances representation learning for both node and graph-level tasks using cross-view contrastive learning.

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    Area of Science:

    • Graph Neural Networks
    • Representation Learning
    • Machine Learning

    Background:

    • Heterogeneous graphs contain rich information but pose challenges for unsupervised and self-supervised learning (SSL).
    • Existing methods often rely on neighbor proximity and struggle to integrate node features or capture global graph properties.
    • There is a need for label-free frameworks that can effectively learn representations from heterogeneous graphs, especially for graph-level tasks.

    Purpose of the Study:

    • To propose a novel self-supervised heterogeneous graph neural network (GNN) framework called HeGCL.
    • To address the limitations of existing methods in capturing global properties and integrating diverse information in heterogeneous graphs.
    • To develop a label-free approach for enhanced representation learning in heterogeneous graphs.

    Main Methods:

    • HeGCL utilizes a cross-view contrastive learning approach with two distinct views: meta-path and outline.
    • The meta-path view captures semantic information, while the outline view encodes complex edge relations using a nonlocal block for graph-level properties.
    • Node embeddings are learned by maximizing mutual information (MI) between the global (outline) and semantic (meta-path) representations.

    Main Results:

    • The proposed HeGCL model demonstrates superior performance on both node-level and graph-level tasks compared to existing methods.
    • Experiments confirm the effectiveness of the cross-view contrastive learning strategy in heterogeneous graph representation learning.
    • The inclusion of the nonlocal block significantly contributes to the model's ability to capture graph-level properties.

    Conclusions:

    • HeGCL offers an effective label-free framework for representation learning in heterogeneous graphs.
    • The dual-view approach, incorporating semantic and global properties, enhances model performance.
    • The nonlocal block is crucial for improving graph-level task performance in heterogeneous graph representation learning.