Generating Analytic Asset Recommendations Using Graph Neural Networks

A computer system for visual analysis of datasets, comprising:

• one or more processors;
• and
• memory;
• wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprising instructions for:
• obtaining a data graph that includes a plurality of nodes, wherein each node stores metadata for a respective analytic asset of a plurality of analytic assets, and the data graph encodes relationships between the plurality of analytic assets;
• extracting a set of features for each node of the data graph;
• deriving corresponding node embeddings for two nodes of the data graph using a two-layer graph neural network based on the data graph and the set of features;
• predicting a link between the two nodes of the data graph based on the corresponding node embeddings;
• and
• generating a recommendation for an analytic asset in accordance with a determination that a probability for the link is above a predetermined threshold.

A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having a display, one or more processors, and memory, the one or more programs comprising instructions for:

• obtaining a data graph that includes a plurality of nodes, wherein each node stores metadata for a respective analytic asset of a plurality of analytic assets, and the data graph encodes relationships between the plurality of analytic assets;
• extracting a set of features for each node of the data graph;
• deriving corresponding node embeddings for two nodes of the data graph using a two-layer graph neural network based on the data graph and the set of features;
• predicting a link between the two nodes of the data graph based on the corresponding node embeddings;
• and
• generating a recommendation for an analytic asset in accordance with a determination that a probability for the link is above a predetermined threshold.