receive, from a client device via the network interface, configuration information for a graph analysis pipeline;

based on the received configuration information, deploy the graph analysis pipeline, wherein the graph analysis pipeline functions to:

obtain an input dataset from either or both of (i) one or more data sources that are internal to the computing platform and (ii) one or more data sources that are external to the computing platform and accessible via one or more networks;

construct a graph from the input dataset;

for a given node within the constructed graph, generate a first type of embedding vector using a first embedding technique and a second type of embedding vector using a second embedding technique that differs from the first embedding technique, wherein the first type is different from the second type; and

input the first and second types of embedding vectors for the given node into a graph data science model that is run on the computing platform and is configured to render a given prediction for the given node by (i) using a first intermediate graph data science model to render a first intermediate prediction based at least in part on the first type of embedding vector, (ii) using a second intermediate graph data science model to render a second intermediate prediction based at least in part on the second type of embedding vector, and (iii) combining the first and second intermediate predictions to produce the given prediction for the given node as an output that comprises a combination of the first and second intermediate predictions, wherein the second intermediate graph data science model is different from the first intermediate graph data science model.