def preprocess(a):
a = normalized_laplacian(a)
a = rescale_laplacian(a)
return a
def preprocess(A):
fltr = normalized_laplacian(A, symmetric=True)
fltr = rescale_laplacian(fltr, lmax=2)
return fltr
def preprocess(A):
L = normalized_laplacian(A)
L = rescale_laplacian(L)
return L
# Parameters
ARMA_T = 1 # Depth of each ARMA_1 filter
ARMA_K = 2 # Number of parallel ARMA_1 filters
recurrent = True # Share weights like a recurrent net in each head
N = X.shape[0] # Number of nodes in the graph
F = X.shape[1] # Original feature dimensionality
n_classes = y.shape[1] # Number of classes
dropout_rate = 0.75 # Dropout rate applied to the input of GCN layers
l2_reg = 5e-4 # Regularization rate for l2
learning_rate = 1e-2 # Learning rate for SGD
epochs = 20000 # Number of training epochs
es_patience = 200 # Patience for early stopping
# Preprocessing operations
fltr = normalized_laplacian(A, symmetric=True)
fltr = rescale_laplacian(fltr, lmax=2)
# Model definition
X_in = Input(shape=(F, ))
fltr_in = Input((N, ), sparse=True)
dropout_1 = Dropout(dropout_rate)(X_in)
graph_conv_1 = ARMAConv(16,
T=ARMA_T,
K=ARMA_K,
recurrent=recurrent,
dropout_rate=dropout_rate,
activation='elu',
gcn_activation='elu',
kernel_regularizer=l2(l2_reg))([dropout_1, fltr_in])
dropout_2 = Dropout(dropout_rate)(graph_conv_1)