[Graphbolt] Polish the quickstart node classification tasks. (#6530)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

examples/sampling/graphbolt/quickstart/node_classification.py

 """
-[Semi-Supervised Classification with Graph Convolutional Networks]
-(https://arxiv.org/abs/1609.02907)
+This example shows how to create a GraphBolt dataloader to sample and train a
+node classification model with the Cora dataset.
 """
 import dgl.graphbolt as gb
 import dgl.nn as dglnn
@@ -15,7 +15,7 @@ import torchmetrics.functional as MF
 ############################################################################
 def create_dataloader(dateset, itemset, device):
     # Sample seed nodes from the itemset.
-    datapipe = gb.ItemSampler(itemset, batch_size=8)
+    datapipe = gb.ItemSampler(itemset, batch_size=16)
 
     # Sample neighbors for the seed nodes.
     datapipe = datapipe.sample_neighbor(dataset.graph, fanouts=[4, 2])
@@ -73,6 +73,7 @@ def evaluate(model, dataset, itemset, device):
 
 
 def train(model, dataset, device):
+    # The first of two tasks in the dataset is node classification.
     task = dataset.tasks[0]
     dataloader = create_dataloader(dataset, task.train_set, device)
     optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
@@ -119,9 +120,12 @@ if __name__ == "__main__":
 
     # Load and preprocess dataset.
     print("Loading data...")
-    dataset = gb.OnDiskDataset(
-        "examples/sampling/graphbolt/quickstart/cora/"
-    ).load()
+    dataset = gb.BuiltinDataset("cora").load()
+
+    # Uncomment to use the example cora dataset.
+    # dataset = gb.OnDiskDataset(
+    #     "examples/sampling/graphbolt/quickstart/cora/"
+    # ).load()
 
     in_size = dataset.feature.size("node", None, "feat")[0]
     out_size = dataset.tasks[0].metadata["num_classes"]