CONDITIONING GRAPH NEURAL NETWORKS ON GRAPH AFFINITY MEASURE FEATURES

    公开(公告)号:US20230281430A1

    公开(公告)日:2023-09-07

    申请号:US18118061

    申请日:2023-03-06

    Applicant: Google LLC

    CPC classification number: G06N3/047 G06N3/082

    Abstract: Methods and systems for conditioning graph neural networks on affinity features. One of the methods includes obtaining graph data representing an input graph that comprises a set of nodes and a set of edges that each connect a respective pair of nodes, the graph data comprising respective node features for each of the nodes, edge features for each of the edges, and a respective weight for each of the edges; generating one or more affinity features, each affinity feature representing a property of one or more random walks through the graph guided by the respective weights for the edges; and processing the graph data using a graph neural network that is conditioned on the one or more affinity features to generate a task prediction for a machine learning task for the input graph.

    Robust Network Path Generation
    3.
    发明公开

    公开(公告)号:US20230388224A1

    公开(公告)日:2023-11-30

    申请号:US17886764

    申请日:2022-08-12

    Applicant: Google LLC

    CPC classification number: H04L45/38 H04L45/22 H04L45/02

    Abstract: Example aspects of the present disclosure provide for an example computer-implemented method for generating alternative network paths, the example method including obtaining a network graph; determining flows respectively for edges of the network graph by: resolving a linear system of weights associated with the edges, the linear system resolved over a reduced network graph, and propagating a solution of the linear system into a respective partition of a plurality of partitions of the network graph to determine at least one of the flows within the respective partition; and determining a plurality of alternative paths across the network graph.

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