16.5.3 Advanced Hyperparameter Customization

You can build an Unsupervised EdgeWise model using rich hyperparameter customization.

This is implemented using the GraphWiseConvLayerConfig sub-config classes.

The following code describes the implementation of the configuration in an Unsupervised EdgeWise model. The example also specifies a weight decay parameter of 0.001 and dropout with dropping probability 0.5 to counteract overfitting.

• JShell
• Java
• Python

opg4j> var weightProperty = analyst.pagerank(trainGraph).getName()
opg4j> var convLayerConfig = analyst.graphWiseConvLayerConfigBuilder().
                setNumSampledNeighbors(25).
                setActivationFunction(ActivationFunction.TANH).
                setWeightInitScheme(WeightInitScheme.XAVIER).
                setWeightedAggregationProperty(weightProperty).
                setDropoutRate(0.5).
                build()
opg4j> var dgiLayerConfig = analyst.graphWiseDgiLayerConfigBuilder().
         setCorruptionFunction(new PermutationCorruption()).
         setDiscriminator(GraphWiseDgiLayerConfig.Discriminator.BILINEAR).
         setReadoutFunction(GraphWiseDgiLayerConfig.ReadoutFunction.MEAN).
         build()
opg4j> var model = analyst.unsupervisedEdgeWiseModelBuilder().
         setVertexInputPropertyNames("vertex_features").
         setEdgeInputPropertyNames("edge_features").
         setConvLayerConfigs(convLayerConfig).
         setDgiLayerConfig(dgiLayerConfig).
         setWeightDecay(0.001).
         build()

String weightProperty = analyst.pagerank(trainGraph).getName();
GraphWiseConvLayerConfig convLayerConfig = analyst.graphWiseConvLayerConfigBuilder()
    .setNumSampledNeighbors(25)
    .setActivationFunction(ActivationFunction.TANH)
    .setWeightInitScheme(WeightInitScheme.XAVIER)
    .setWeightedAggregationProperty(weightProperty)
    .setDropoutRate(0.5)
    .build();

GraphWiseDgiLayerConfig dgiLayerConfig = analyst.graphWiseDgiLayerConfigBuilder()
    .setCorruptionFunction(new PermutationCorruption())
    .setDiscriminator(GraphWiseDgiLayerConfig.Discriminator.BILINEAR)
    .setReadoutFunction(GraphWiseDgiLayerConfig.ReadoutFunction.MEAN)
    .build();

UnsupervisedEdgeWiseModel model = analyst.unsupervisedEdgeWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_features")
    .setEdgeInputPropertyNames("edge_features")
    .setConvLayerConfigs(convLayerConfig)
    .setDgiLayerConfigs(dgiLayerConfig)
    .setWeightDecay(0.001)
    .build();

weightProperty = analyst.pagerank(train_graph).name

conv_layer_config = dict(num_sampled_neighbors=25,
                         activation_fn='tanh',
                         weight_init_scheme='xavier',
                         neighbor_weight_property_name=weightProperty,
                         dropout_rate=0.5)

conv_layer = analyst.graphwise_conv_layer_config(**conv_layer_config)

dgi_layer_config = dict(corruption_function=None,
                        readout_function="mean",
                        discriminator="bilinear")

dgi_layer = analyst.graphwise_dgi_layer_config(**dgi_layer_config)

params = dict(conv_layer_config=[conv_layer],
              dgi_layer_config=dgi_layer,
              loss_fn="sigmoid_cross_entropy",
              vertex_input_property_names=["vertex_features"],
              edge_input_property_names=["edge_features"],
              seed=17,
              weight_decay=0.001)

model = analyst.unsupervised_edgewise_builder(**params)