wehub-resource-sync
115 lines
3.1 KiB
Python
115 lines
3.1 KiB
Python
"""
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[Predict then Propagate: Graph Neural Networks meet Personalized PageRank]
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(https://arxiv.org/abs/1810.05997)
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"""
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import dgl.sparse as dglsp
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from dgl.data import CoraGraphDataset
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from torch.optim import Adam
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class APPNP(nn.Module):
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def __init__(
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self,
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in_size,
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out_size,
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hidden_size=64,
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dropout=0.1,
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num_hops=10,
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alpha=0.1,
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):
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super().__init__()
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self.f_theta = nn.Sequential(
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nn.Dropout(dropout),
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nn.Linear(in_size, hidden_size),
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nn.ReLU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_size, out_size),
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)
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self.num_hops = num_hops
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self.A_dropout = nn.Dropout(dropout)
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self.alpha = alpha
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def forward(self, A_hat, X):
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Z_0 = Z = self.f_theta(X)
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for _ in range(self.num_hops):
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A_drop = dglsp.val_like(A_hat, self.A_dropout(A_hat.val))
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Z = (1 - self.alpha) * A_drop @ Z + self.alpha * Z_0
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return Z
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def evaluate(g, pred):
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label = g.ndata["label"]
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val_mask = g.ndata["val_mask"]
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test_mask = g.ndata["test_mask"]
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# Compute accuracy on validation/test set.
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val_acc = (pred[val_mask] == label[val_mask]).float().mean()
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test_acc = (pred[test_mask] == label[test_mask]).float().mean()
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return val_acc, test_acc
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def train(model, g, A_hat, X):
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label = g.ndata["label"]
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train_mask = g.ndata["train_mask"]
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optimizer = Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
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for epoch in range(50):
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# Forward.
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model.train()
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logits = model(A_hat, X)
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# Compute loss with nodes in training set.
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loss = F.cross_entropy(logits[train_mask], label[train_mask])
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# Backward.
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# Compute prediction.
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model.eval()
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logits = model(A_hat, X)
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pred = logits.argmax(dim=1)
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# Evaluate the prediction.
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val_acc, test_acc = evaluate(g, pred)
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print(
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f"In epoch {epoch}, loss: {loss:.3f}, val acc: {val_acc:.3f}, test"
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f" acc: {test_acc:.3f}"
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)
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if __name__ == "__main__":
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# If CUDA is available, use GPU to accelerate the training, use CPU
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# otherwise.
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dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Load graph from the existing dataset.
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dataset = CoraGraphDataset()
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g = dataset[0].to(dev)
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# Create the sparse adjacency matrix A.
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indices = torch.stack(g.edges())
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N = g.num_nodes()
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A = dglsp.spmatrix(indices, shape=(N, N))
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# Calculate the symmetrically normalized adjacency matrix.
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I = dglsp.identity(A.shape, device=dev)
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A_hat = A + I
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D_hat = dglsp.diag(A_hat.sum(dim=1)) ** -0.5
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A_hat = D_hat @ A_hat @ D_hat
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# Create APPNP model.
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X = g.ndata["feat"]
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in_size = X.shape[1]
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out_size = dataset.num_classes
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model = APPNP(in_size, out_size).to(dev)
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# Kick off training.
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train(model, g, A_hat, X)
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