def _preprocess_features(self, features):
""" The propagation step is made here"""
if self.normalize_features:
features = row_normalize(features)
for i in range(self.num_layers):
features = self.graph_adj @ features
return to_sparse_tensor(features)
def _preprocess_features(self, features):
""" The propagation step is made here"""
if self.normalize_features:
features = row_normalize(features)
initial_features = features.copy()
alpha = np.float32(self.teleport_prob)
for i in range(self.num_layers):
features = (
1 -
alpha) * self.graph_adj @ features + alpha * initial_features
return to_sparse_tensor(features)
def _preprocess_features(self, features):
if self.normalize_features:
features = row_normalize(features)
return to_sparse_tensor(features)
def _preprocess_features(self, features):
if self.normalize_features:
features = row_normalize(features)
return tf.constant(features.todense())