graph.py

from __future__ import print_function

from keras import activations, initializers, constraints
from keras import regularizers
from keras.engine import Layer
import keras.backend as K


class GraphConvolution(Layer):
    """Basic graph convolution layer as in https://arxiv.org/abs/1609.02907"""
    def __init__(self, units,  support=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform', #Gaussian distribution 
                 bias_initializer='zeros',
                 kernel_regularizer=None, 
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        if 'input_shape' not in kwargs and 'input_dim' in kwargs:
            kwargs['input_shape'] = (kwargs.pop('input_dim'),)
        super(GraphConvolution, self).__init__(**kwargs)
        self.units = units
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.supports_masking = True

        self.support = support
        assert support >= 1

    def compute_output_shape(self, input_shapes):
        features_shape = input_shapes[0]
        output_shape = (features_shape[0], self.units)
        return output_shape  # (batch_size, output_dim)

    def build(self, input_shapes):
        features_shape = input_shapes[0]
        assert len(features_shape) == 2
        input_dim = features_shape[1]

        self.kernel = self.add_weight(shape=(input_dim * self.support,self.units),
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.units,),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
        self.built = True

    def call(self, inputs, mask=None):
        features = inputs[0] # X_in =X
        basis = inputs[1:]  # G= A_

        supports = list()
        for i in range(self.support):
            supports.append(K.dot(basis[i], features))
        supports = K.concatenate(supports, axis=1)
        output = K.dot(supports, self.kernel)

        if self.bias:
            output += self.bias
        return self.activation(output)