[Sparse] SGC example (#4918)

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Co-authored-by: Ubuntu <ubuntu@ip-172-31-36-188.ap-northeast-1.compute.internal>

examples/sparse/sgc.py

+"""
+[Simplifying Graph Convolutional Networks]
+(https://arxiv.org/abs/1902.07153)
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl.data import CoraGraphDataset
+from dgl.mock_sparse import create_from_coo, diag, identity
+from torch.optim import Adam
+
+
+################################################################################
+# (HIGHLIGHT) Take the advantage of DGL sparse APIs to implement the feature
+# pre-computation.
+################################################################################
+def pre_compute(A, X, k):
+    for _ in range(k):
+        X = A @ X
+    return X
+
+
+def evaluate(g, pred):
+    label = g.ndata["label"]
+    val_mask = g.ndata["val_mask"]
+    test_mask = g.ndata["test_mask"]
+
+    # Compute accuracy on validation/test set.
+    val_acc = (pred[val_mask] == label[val_mask]).float().mean()
+    test_acc = (pred[test_mask] == label[test_mask]).float().mean()
+    return val_acc, test_acc
+
+
+def train(g, X_sgc, model):
+    label = g.ndata["label"]
+    train_mask = g.ndata["train_mask"]
+    optimizer = Adam(model.parameters(), lr=2e-1, weight_decay=5e-6)
+
+    for epoch in range(20):
+        # Forward.
+        logits = model(X_sgc)
+
+        # Compute loss with nodes in the training set.
+        loss = F.cross_entropy(logits[train_mask], label[train_mask])
+
+        # Backward.
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # Compute prediction.
+        pred = logits.argmax(dim=1)
+
+        # Evaluate the prediction.
+        val_acc, test_acc = evaluate(g, pred)
+        print(
+            f"In epoch {epoch}, loss: {loss:.3f}, val acc: {val_acc:.3f}, test"
+            f" acc: {test_acc:.3f}"
+        )
+
+
+if __name__ == "__main__":
+    # If CUDA is available, use GPU to accelerate the training, use CPU
+    # otherwise.
+    dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    # Load graph from the existing dataset.
+    dataset = CoraGraphDataset()
+    g = dataset[0].to(dev)
+
+    # Create the sparse adjacency matrix A
+    src, dst = g.edges()
+    N = g.num_nodes()
+    A = create_from_coo(dst, src, shape=(N, N))
+
+    # Calculate the symmetrically normalized adjacency matrix.
+    I = identity(A.shape, device=dev)
+    A_hat = A + I
+    D_hat = diag(A_hat.sum(dim=1)) ** -0.5
+    A_hat = D_hat @ A_hat @ D_hat
+
+    # 2-hop diffusion.
+    k = 2
+    X = g.ndata["feat"]
+    X_sgc = pre_compute(A_hat, X, k)
+
+    # Create model.
+    in_size = X.shape[1]
+    out_size = dataset.num_classes
+    model = nn.Linear(in_size, out_size).to(dev)
+
+    # Kick off training.
+    train(g, X_sgc, model)

examples/sparse/sign.py

@@ -9,10 +9,10 @@ operator on top of symmetrically normalized adjacency matrix A_hat.
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.optim import Adam
-
 from dgl.data import CoraGraphDataset
 from dgl.mock_sparse import create_from_coo, diag, identity
+from torch.optim import Adam
+
 
 ################################################################################
 # (HIGHLIGHT) Take the advantage of DGL sparse APIs to implement the feature