Scalable Self-Supervised Graph Clustering
    1.
    发明公开

    公开(公告)号:US20240176993A1

    公开(公告)日:2024-05-30

    申请号:US18485457

    申请日:2023-10-12

    Applicant: Google LLC

    CPC classification number: G06N3/0464 G06N3/0895

    Abstract: A method of training a machine learning model includes receiving training data comprising a graph structure and one or more feature attributes and determining an encoded graph based on applying the machine learning model to the graph structure and the one or more feature attributes. The machine learning model comprises a graph convolutional network layer. The encoded graph comprises one or more nodes and one or more paths connecting the one or more nodes. The method also includes selecting a plurality of positive samples through random walks along the one or more paths of the encoded graph, selecting a plurality of negative samples from the encoded graph by randomly sampling the one or more nodes of the encoded graph, determining a loss value, and updating, based on the loss value, one or more learnable parameter values of the machine learning model.

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