Why is the adjacency matrix taken in to graph convolutional layers each time? and self loops

Hi!

I am building a graph convolutional network which will be used in conjunction with a merged layer for a reinforcement learning task.

I have a technical question about the convolutional layer itself which is slightly confusing to me which is: why is the adjacency matrix passed in to each conv layer and not ONLY the first one? My code is as follows:

adj = nx.to_numpy_array(graph)

node_features = [] #just the degree of the graph nodes
node_degree = nx.degree(damage_graph)
for i in dict(node_degree).values():
    node_features.append(i / len(damage_graph))

node_features_final = np.array(node_features).reshape(-1, 1)


adj_normalised = normalized_adjacency(adj)
adj_normalised = sp_matrix_to_sp_tensor(adj_normalised)
node_feature_shape = 1


nodefeature_input = tf.keras.layers.Input(shape=(node_feature_shape,), name='node_features_input')
adjacency_input = tf.keras.layers.Input(shape=(None,), name='adjacency_input', sparse=True)

conv_layer_one = GCNConv(64, activation='relu')([nodefeature_input, adj_normalised])
conv_layer_one = tf.keras.layers.Dropout(0.2)(conv_layer_one)
conv_layer_two = GCNConv(32, activation='relu')([conv_layer_one, adj_normalised])
conv_layer_pool = GlobalAvgPool()(conv_layer_two)
dense_layer_graph = tf.keras.layers.Dense(128, activation='relu')(conv_layer_pool)

input_action_vector = tf.keras.layers.Input(shape=(action_vector,), name='action_vec_input')
action_vector_dense = tf.keras.layers.Dense(128, activation='relu', name='action_layer_dense')(input_action_vector)

merged_layer = tf.keras.layers.Concatenate()([dense_layer_graph, action_vector_dense])
#output_layer... etc
model = Model([nodefeature_input, adjacency_input], [output_layer])

and my second question is about the normalise_adjacency - it does not add self loops. Should self loops be added before or after normalising the matrix?

thank you!

[amjass12]

perfect, thank you very much!! I appreciate all your time 😃

[amjass12]

sorry @danielegrattarola - yes you are right:

it is:

adj = nx.adjacency_matrix(damage_graph)
adj_preprocessed = GCNConv.preprocess(adj)
adj_preprocessed = sp_matrix_to_sp_tensor(adj_preprocessed)

without the last line, it was giving me a datatype error, so, sp_matrix_to_sp_tensor is necessary after the GCNConv.preprocess line!