Large-Scale Architecture Search in Graph Neural Networks via Synthetic Data

A computer-implemented method, the method comprising:

• generating, by a computing system comprising one or more processors, a plurality of synthetic graph datasets, wherein the plurality of synthetic graph datasets comprise structured-graph data;
• training, by the computing system, a plurality of graph models with at least a subset of the plurality of synthetic graph datasets to generate a plurality of trained graph models;
• processing, by the computing system, one or more inputs from the plurality of synthetic graph datasets with the plurality of trained graph models to generate a plurality of graph outputs;
• generating, by the computing system, an evaluation representation associated with the plurality of graph models based on the plurality of graph outputs;
• and
• providing, by the computing system, the evaluation representation for display.

One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising:

• obtaining input data associated with a user;
• generating, by one or more generators, a plurality of synthetic graph datasets based at least in part on the input data, wherein the plurality of synthetic graph datasets comprise structured-graph data;
• training a plurality of graph models with at least a subset of the plurality of synthetic graph datasets to generate a plurality of trained graph models;
• processing one or more inputs from the plurality of synthetic graph datasets with the plurality of trained graph models to generate a plurality of graph outputs;
• generating an output representation associated with the plurality of graph models based on the plurality of graph outputs;
• and
• providing the output representation for display.