[GraphBolt] Update notebook examples. (#7266)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>

notebooks/stochastic_training/link_prediction.ipynb

@@ -77,7 +77,7 @@
       },
       "outputs": [],
       "source": [
-        "dataset = gb.BuiltinDataset(\"cora\").load()"
+        "dataset = gb.BuiltinDataset(\"cora-seeds\").load()"
       ]
     },
     {
@@ -255,7 +255,8 @@
         "    total_loss = 0\n",
         "    for step, data in tqdm(enumerate(create_train_dataloader())):\n",
         "        # Get node pairs with labels for loss calculation.\n",
-        "        compacted_pairs, labels = data.node_pairs_with_labels\n",
+        "        compacted_seeds = data.compacted_seeds.T\n",
+        "        labels = data.labels\n",
         "        node_feature = data.node_features[\"feat\"]\n",
         "        # Convert sampled subgraphs to DGL blocks.\n",
         "        blocks = data.blocks\n",
@@ -263,7 +264,7 @@
         "        # Get the embeddings of the input nodes.\n",
         "        y = model(blocks, node_feature)\n",
         "        logits = model.predictor(\n",
-        "            y[compacted_pairs[0]] * y[compacted_pairs[1]]\n",
+        "            y[compacted_seeds[0]] * y[compacted_seeds[1]]\n",
         "        ).squeeze()\n",
         "\n",
         "        # Compute loss.\n",
@@ -308,7 +309,8 @@
         "labels = []\n",
         "for step, data in tqdm(enumerate(eval_dataloader)):\n",
         "    # Get node pairs with labels for loss calculation.\n",
-        "    compacted_pairs, label = data.node_pairs_with_labels\n",
+        "    compacted_seeds = data.compacted_seeds.T\n",
+        "    label = data.labels\n",
         "\n",
         "    # The features of sampled nodes.\n",
         "    x = data.node_features[\"feat\"]\n",
@@ -316,7 +318,7 @@
         "    # Forward.\n",
         "    y = model(data.blocks, x)\n",
         "    logit = (\n",
-        "        model.predictor(y[compacted_pairs[0]] * y[compacted_pairs[1]])\n",
+        "        model.predictor(y[compacted_seeds[0]] * y[compacted_seeds[1]])\n",
         "        .squeeze()\n",
         "        .detach()\n",
         "    )\n",

notebooks/stochastic_training/node_classification.ipynb

@@ -78,7 +78,7 @@
       },
       "outputs": [],
       "source": [
-        "dataset = gb.BuiltinDataset(\"ogbn-arxiv\").load()"
+        "dataset = gb.BuiltinDataset(\"ogbn-arxiv-seeds\").load()"
       ]
     },
     {
@@ -143,7 +143,7 @@
       "source": [
         "def create_dataloader(itemset, shuffle):\n",
         "    datapipe = gb.ItemSampler(itemset, batch_size=1024, shuffle=shuffle)\n",
-        "    datapipe = datapipe.copy_to(device, extra_attrs=[\"seed_nodes\"])\n",
+        "    datapipe = datapipe.copy_to(device, extra_attrs=[\"seeds\"])\n",
         "    datapipe = datapipe.sample_neighbor(graph, [4, 4])\n",
         "    datapipe = datapipe.fetch_feature(feature, node_feature_keys=[\"feat\"])\n",
         "    return gb.DataLoader(datapipe)"
@@ -375,4 +375,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
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