#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.(https://arxiv.org/pdf/1609.02907)
"""
import tensorflow as tf
from tensorflow.python.keras.initializers import Identity, glorot_uniform, Zeros
from tensorflow.python.keras.layers import Dropout, Input, Layer, Embedding, Reshape
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.regularizers import l2
class GraphConvolution(Layer): # ReLU(AXW)
def __init__(self, units,
activation=tf.nn.relu, dropout_rate=0.5,
use_bias=True, l2_reg=0, feature_less=False,
seed=1024, **kwargs):
super(GraphConvolution, self).__init__(**kwargs)
self.units = units
self.feature_less = feature_less
self.use_bias = use_bias
self.l2_reg = l2_reg
self.dropout_rate = dropout_rate
self.activation = activation
self.seed = seed
def build(self, input_shapes):
if self.feature_less:
input_dim = int(input_shapes[0][-1])
else:
assert len(input_shapes) == 2
features_shape = input_shapes[0]
input_dim = int(features_shape[-1])
self.kernel = self.add_weight(shape=(input_dim,
self.units),
initializer=glorot_uniform(
seed=self.seed),
regularizer=l2(self.l2_reg),
name='kernel', )
if self.use_bias:
self.bias = self.add_weight(shape=(self.units,),
initializer=Zeros(),
name='bias', )
self.dropout = Dropout(self.dropout_rate, seed=self.seed)
self.built = True
def call(self, inputs, training=None, **kwargs):
features, A = inputs
features = self.dropout(features, training=training)
output = tf.matmul(tf.sparse_tensor_dense_matmul(
A, features), self.kernel)
if self.bias:
output += self.bias
act = self.activation(output)
act._uses_learning_phase = features._uses_learning_phase
return act
def get_config(self):
config = {'units': self.units,
'activation': self.activation,
'dropout_rate': self.dropout_rate,
'l2_reg': self.l2_reg,
'use_bias': self.use_bias,
'feature_less': self.feature_less,
'seed': self.seed
}
base_config = super(GraphConvolution, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def GCN(adj_dim,feature_dim,n_hidden, num_class, num_layers=2,activation=tf.nn.relu,dropout_rate=0.5, l2_reg=0, feature_less=True, ):
Adj = Input(shape=(None,), sparse=True)
if feature_less:
X_in = Input(shape=(1,), )
emb = Embedding(adj_dim, feature_dim,
embeddings_initializer=Identity(1.0), trainable=False)
X_emb = emb(X_in)
h = Reshape([X_emb.shape[-1]])(X_emb)
else:
X_in = Input(shape=(feature_dim,), )
h = X_in
for i in range(num_layers):
if i == num_layers - 1:
activation = tf.nn.softmax
n_hidden = num_class
h = GraphConvolution(n_hidden, activation=activation, dropout_rate=dropout_rate, l2_reg=l2_reg)([h,Adj])
output = h
model = Model(inputs=[X_in,Adj], outputs=output)
return model
