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16 | 17 | To run the model, you need to have at least Python 3.6 and install following packages: 18 | 19 | ```bash 20 | PyTorch 1.6.0 21 | PyTorch Geometric 1.6.1 22 | tqdm 23 | sklearn 24 | ``` 25 | -------------------------------------------------------------------------------- /data/PROTEINS/raw/README.txt: -------------------------------------------------------------------------------- 1 | README for dataset PROTEINS 2 | 3 | 4 | === Usage === 5 | 6 | This folder contains the following comma separated text files 7 | (replace DS by the name of the dataset): 8 | 9 | n = total number of nodes 10 | m = total number of edges 11 | N = number of graphs 12 | 13 | (1) DS_A.txt (m lines) 14 | sparse (block diagonal) adjacency matrix for all graphs, 15 | each line corresponds to (row, col) resp. (node_id, node_id) 16 | 17 | (2) DS_graph_indicator.txt (n lines) 18 | column vector of graph identifiers for all nodes of all graphs, 19 | the value in the i-th line is the graph_id of the node with node_id i 20 | 21 | (3) DS_graph_labels.txt (N lines) 22 | class labels for all graphs in the dataset, 23 | the value in the i-th line is the class label of the graph with graph_id i 24 | 25 | (4) DS_node_labels.txt (n lines) 26 | column vector of node labels, 27 | the value in the i-th line corresponds to the node with node_id i 28 | 29 | There are OPTIONAL files if the respective information is available: 30 | 31 | (5) DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt) 32 | labels for the edges in DS_A_sparse.txt 33 | 34 | (6) DS_edge_attributes.txt (m lines; same size as DS_A.txt) 35 | attributes for the edges in DS_A.txt 36 | 37 | (7) DS_node_attributes.txt (n lines) 38 | matrix of node attributes, 39 | the comma seperated values in the i-th line is the attribute vector of the node with node_id i 40 | 41 | (8) DS_graph_attributes.txt (N lines) 42 | regression values for all graphs in the dataset, 43 | the value in the i-th line is the attribute of the graph with graph_id i 44 | 45 | 46 | === Previous Use of the Dataset === 47 | 48 | Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., Borgwardt, K.M.: Scalable 49 | kernels for graphs with continuous attributes. In: C.J.C. Burges, L. Bottou, Z. Ghahra- 50 | mani, K.Q. Weinberger (eds.) NIPS, pp. 216-224 (2013) 51 | 52 | Neumann, M., Garnett R., Bauckhage Ch., Kersting K.: Propagation Kernels: Efficient Graph 53 | Kernels from Propagated Information. Under review at MLJ. 54 | 55 | 56 | === References === 57 | 58 | K. M. Borgwardt, C. S. Ong, S. Schoenauer, S. V. N. Vishwanathan, A. J. Smola, and H. P. 59 | Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(Suppl 1):i47–i56, 60 | Jun 2005. 61 | 62 | P. D. Dobson and A. J. Doig. Distinguishing enzyme structures from non-enzymes without 63 | alignments. J. Mol. Biol., 330(4):771–783, Jul 2003. 64 | 65 | 66 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | """ 2 | The Pytorch-Geometric Implementation of GCNN model from the following paper 3 | "Fake News Detection on Social Media using Geometric Deep Learning" 4 | https://arxiv.org/abs/1902.06673 5 | """ 6 | 7 | import argparse 8 | import time 9 | from tqdm import tqdm 10 | 11 | import torch 12 | import torch.nn.functional as F 13 | from torch.utils.data import random_split 14 | from torch_geometric.data import DataLoader 15 | from torch.nn import Linear 16 | from torch_geometric.nn import global_mean_pool, GATConv 17 | from torch_geometric.datasets import TUDataset 18 | 19 | from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score 20 | 21 | parser = argparse.ArgumentParser() 22 | 23 | # original model parameters 24 | parser.add_argument('--seed', type=int, default=777, help='random seed') 25 | parser.add_argument('--device', type=str, default='cuda:0', help='specify cuda devices') 26 | 27 | # hyper-parameters 28 | parser.add_argument('--dataset', type=str, default='PROTEINS', help='DD/PROTEINS/NCI1/NCI109/Mutagenicity/ENZYMES') 29 | parser.add_argument('--batch_size', type=int, default=128, help='batch size') 30 | parser.add_argument('--lr', type=float, default=0.001, help='learning rate') 31 | parser.add_argument('--weight_decay', type=float, default=0.01, help='weight decay') 32 | parser.add_argument('--nhid', type=int, default=128, help='hidden size') 33 | parser.add_argument('--epochs', type=int, default=100, help='maximum number of epochs') 34 | 35 | args = parser.parse_args() 36 | torch.manual_seed(args.seed) 37 | if torch.cuda.is_available(): 38 | torch.cuda.manual_seed(args.seed) 39 | 40 | dataset = TUDataset('data/', name=args.dataset, use_node_attr=True) 41 | 42 | args.num_classes = dataset.num_classes 43 | args.num_features = dataset.num_features 44 | 45 | print(args) 46 | 47 | num_training = int(len(dataset) * 0.6) 48 | num_val = int(len(dataset) * 0.1) 49 | num_test = len(dataset) - (num_training + num_val) 50 | training_set, validation_set, test_set = random_split(dataset, [num_training, num_val, num_test]) 51 | 52 | train_loader = DataLoader(training_set, batch_size=args.batch_size, shuffle=True) 53 | val_loader = DataLoader(validation_set, batch_size=args.batch_size, shuffle=False) 54 | test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False) 55 | 56 | 57 | class Net(torch.nn.Module): 58 | def __init__(self): 59 | super(Net, self).__init__() 60 | 61 | self.num_features = dataset.num_features 62 | self.nhid = args.nhid 63 | 64 | self.conv1 = GATConv(self.num_features, self.nhid * 2) 65 | self.conv2 = GATConv(self.nhid * 2, self.nhid * 2) 66 | 67 | self.fc1 = Linear(self.nhid * 2, self.nhid) 68 | self.fc2 = Linear(self.nhid, dataset.num_classes) 69 | 70 | def forward(self, x, edge_index, batch): 71 | 72 | x = F.selu(self.conv1(x, edge_index)) 73 | x = F.selu(self.conv2(x, edge_index)) 74 | x = F.selu(global_mean_pool(x, batch)) 75 | x = F.selu(self.fc1(x)) 76 | x = F.dropout(x, p=0.5, training=self.training) 77 | x = self.fc2(x) 78 | return F.log_softmax(x, dim=-1) 79 | 80 | 81 | def eval(log): 82 | 83 | accuracy, f1_macro, precision, recall = 0, 0, 0, 0 84 | 85 | prob_log, label_log = [], [] 86 | 87 | for batch in log: 88 | pred_y, y = batch[0].data.cpu().numpy().argmax(axis=1), batch[1].data.cpu().numpy().tolist() 89 | prob_log.extend(batch[0].data.cpu().numpy()[:, 1].tolist()) 90 | label_log.extend(y) 91 | 92 | accuracy += accuracy_score(y, pred_y) 93 | f1_macro += f1_score(y, pred_y, average='macro') 94 | precision += precision_score(y, pred_y, zero_division=0) 95 | recall += recall_score(y, pred_y, zero_division=0) 96 | 97 | return accuracy/len(log), f1_macro/len(log), precision/len(log), recall/len(log) 98 | 99 | 100 | def compute_test(loader): 101 | model.eval() 102 | loss_test = 0.0 103 | out_log = [] 104 | with torch.no_grad(): 105 | for data in loader: 106 | data = data.to(args.device) 107 | out = model(data.x, data.edge_index, data.batch) 108 | y = data.y 109 | out_log.append([F.softmax(out, dim=1), y]) 110 | loss_test += F.nll_loss(out, y).item() 111 | return eval(out_log), loss_test 112 | 113 | 114 | model = Net().to(args.device) 115 | model = model.to(args.device) 116 | optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) 117 | 118 | 119 | if __name__ == '__main__': 120 | # Model training 121 | out_log = [] 122 | 123 | t = time.time() 124 | model.train() 125 | for epoch in tqdm(range(args.epochs)): 126 | loss_train = 0.0 127 | correct = 0 128 | for i, data in enumerate(train_loader): 129 | optimizer.zero_grad() 130 | data = data.to(args.device) 131 | out = model(data.x, data.edge_index, data.batch) 132 | y = data.y 133 | loss = F.nll_loss(out, y) 134 | loss.backward() 135 | optimizer.step() 136 | loss_train += loss.item() 137 | out_log.append([F.softmax(out, dim=1), y]) 138 | acc_train, _, _, recall_train = eval(out_log) 139 | [acc_val, _, _, recall_val], loss_val = compute_test(val_loader) 140 | print(f'loss_train: {loss_train:.4f}, acc_train: {acc_train:.4f},' 141 | f' recall_train: {recall_train:.4f}, loss_val: {loss_val:.4f},' 142 | f' acc_val: {acc_val:.4f}, recall_val: {recall_val:.4f}') 143 | 144 | [acc, f1_macro, precision, recall], test_loss = compute_test(test_loader) 145 | print(f'Test set results: acc: {acc:.4f}, f1_macro: {f1_macro:.4f}, ' 146 | f'precision: {precision:.4f}, recall: {recall:.4f}') 147 | -------------------------------------------------------------------------------- /data/PROTEINS/raw/PROTEINS_graph_labels.txt: -------------------------------------------------------------------------------- 1 | 1 2 | 1 3 | 1 4 | 1 5 | 1 6 | 1 7 | 1 8 | 1 9 | 1 10 | 1 11 | 1 12 | 1 13 | 1 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