def get_data(args):
train_data, test_data, train_neg, test_neg, node_embeddings, \
num_nodes, laplacian, spectrum, neighbors, \
neighbors_count = build_data(args.data, args.split_ratio, args.node_out_dim[0])
corpus = Corpus(num_nodes, train_data, test_data, train_neg, test_neg,
len(train_data[0]), laplacian, spectrum, neighbors,
neighbors_count)
return corpus, torch.FloatTensor(node_embeddings)
def getL():
(train_data,train_adjacency_mat), test_data, train_neg, \
test_neg, node_embeddings, num_nodes = build_data(args.data, args.split_ratio)
G = np.zeros((num_nodes, num_nodes), dtype=int)
row, col = train_adjacency_mat
G[row, col] = 1
G[col, row] = 1
A = np.matrix(G)
G = nx.from_numpy_matrix(A)
N = laplacian_matrix(G)
return N