karaph_gcn_1.py

# get adjacency and degree matrix
order = sorted(list(G.nodes())) 

A = nx.to_numpy_matrix(G, nodelist=order) # adjacency matrix

I = np.eye(G.number_of_nodes()) 
A_hat = A + I # 有自循環的相鄰矩陣
D_hat = np.array(np.sum(A_hat, 1)).reshape(-1) 
D_hat = np.matrix(np.diag(D_hat))

hidden_size = 10
N = G.number_of_nodes()

X = np.eye(N) #沒有node feature --> 直接用單位矩陣
W_1 = np.random.normal(loc=0, scale=1, size=(N, hidden_size)) 
W_2 = np.random.normal(loc=0,scale=1,  size=(W_1.shape[1], 2)) # assume it's binary classifier

print('adjacency matrix size:',A_hat.shape)
print('feature matrix size:',X.shape)
print('first layer weight matrix:',W_1.shape)
print('second layer weight matrix:',W_2.shape)