import matplotlib.pyplot as plt import networkx as nx import gcn_utils adj, _, labels, _, _, _ = gcn_utils.load_data('cora') # Pre-processing for Networkx nx_adj = nx.from_numpy_array(adj.ceil().numpy()) node_colour = labels.numpy() / max(labels) labels = dict( (node, label) for node, label in zip(range(len(labels)), labels.numpy())) # Set options options = { 'node_size': 35, 'font_size': 3, 'width': 1.0, 'alpha': 0.3, 'labels': labels, 'node_color': node_colour } # Draw graph nx.draw_spectral(nx_adj, **options) # nx.draw(nx_adj, **options) plt.show()
help='Alpha for the leaky_relu.') parser.add_argument('--patience', type=int, default=100, help='Patience') # args = parser.parse_args([]) args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) # Load data adj, features, labels, idx_train, idx_val, idx_test = gcn_utils.load_data( args.dataset) # Model and optimizer if args.model == 'GAT': model = GAT(nfeat=features.shape[1], nhid=args.hidden, nclass=int(labels.max()) + 1, dropout=args.dropout, nheads=args.nb_heads, alpha=args.alpha) elif args.model == 'GCN': model = GCN(nfeat=features.shape[1], nhid=args.hidden, nclass=int(labels.max()) + 1, dropout=args.dropout) else:
help='Number of hidden units.') parser.add_argument('--dropout', type=float, default=0.5, help='Dropout rate (1 - keep probability).') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) # Load data adj, features, labels, idx_train, idx_val, idx_test = load_data() # Model and optimizer model = GCN(nfeat=features.shape[1], nhid=args.hidden, nclass=labels.max().item() + 1, dropout=args.dropout) optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) if args.cuda: model.cuda() features = features.cuda() adj = adj.cuda() labels = labels.cuda()