1. Graph Developer's Guide for Property Graph
  2. Using the Graph Server (PGX)
  3. Using the Machine Learning Library (PgxML) for Graphs
  4. Using the Supervised EdgeWise Algorithm (Edge Embeddings and Classification)
  5. Building a Minimal Supervised EdgeWise Model

17.3.2 Building a Minimal Supervised EdgeWise Model

You can build an EdgeWise model using the minimal configuration and default hyper-parameters as described in the following code. Note that even though only one feature property is needed (either on vertices with setVertexInputPropertyNames or edges with setEdgeInputPropertyNames) for the model to work, you can specify as many as required. 

opg4j> var model = analyst.supervisedEdgeWiseModelBuilder().
         setVertexInputPropertyNames("vertex_features").
         setEdgeInputPropertyNames("edge_features").
         setEdgeTargetPropertyName("label").
         build()
SupervisedEdgeWiseModel model = analyst.supervisedEdgeWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_features")
    .setEdgeInputPropertyNames("edge_features")
    .setEdgeTargetPropertyName("labels")
    .build();
params = dict(edge_target_property_name="label",
              vertex_input_property_names=["vertex_features"],
              edge_input_property_names=["edge_features"])

model = analyst.supervised_edgewise_builder(**params)

Parent topic: Using the Supervised EdgeWise Algorithm (Edge Embeddings and Classification)