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