diff --git a/models/training-tuning-scripts/fraud-detection-models/gnn-fraud-detection-training.ipynb b/models/training-tuning-scripts/fraud-detection-models/gnn-fraud-detection-training.ipynb
index 25ab125a6e..bd3ce6a285 100644
--- a/models/training-tuning-scripts/fraud-detection-models/gnn-fraud-detection-training.ipynb
+++ b/models/training-tuning-scripts/fraud-detection-models/gnn-fraud-detection-training.ipynb
@@ -86,17 +86,26 @@
     "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#device"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Load training and test dataset"
+    "##### Load traing and test dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,13 +144,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "train_data = augment_data(train_data, n=20)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rearage test data index\n",
+    "last_train_index = train_data.index.max()+1\n",
+    "inductive_data.index = np.arange(last_train_index, last_train_index + inductive_data.shape[0])\n",
+    "inductive_data['index'] = inductive_data.index"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -153,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -178,12 +199,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# train_data, test_data, train_index, test_index, labels, all_data\n",
-    "train_data, test_data, train_idx, inductive_idx, labels, df = prepare_data(train_data, inductive_data)"
+    "# Split trainig, testing dataset\n",
+    "train_data, test_data, train_idx, inductive_idx, labels, df = prepare_data(train_data, inductive_data)\n"
    ]
   },
   {
@@ -204,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,16 +261,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Hyperparameters\n",
     "target_node = \"transaction\"\n",
-    "epochs = 20\n",
+    "epochs = 25\n",
     "in_size, hidden_size, out_size, n_layers,\\\n",
     "    embedding_size = 111, 64, 2, 2, 1\n",
-    "batch_size = 100\n",
+    "batch_size = 256\n",
     "in_size, hidden_size, out_size, n_layers, embedding_size = 111, 64, 2, 2, 1\n",
     "hyperparameters = {\n",
     "    \"in_size\": in_size,\n",
@@ -267,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,309 +306,384 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
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+      "  0%|          | 0/25 [00:00<?, ?it/s]/home/efajardo/miniconda3/envs/morpheus/lib/python3.10/site-packages/dgl/backend/pytorch/tensor.py:445: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
       "  assert input.numel() == input.storage().size(), (\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0/25 | Train Accuracy: 1.0 | Train Loss: 7.589520657285958\n",
+      "Validation Accuracy: 0.9207547307014465 auc 0.20833333333333334\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/25 | Train Accuracy: 1.0 | Train Loss: 118.13089177341317\n",
+      "Validation Accuracy: 0.9207547307014465 auc 0.7851288056206087\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/25 | Train Accuracy: 1.0 | Train Loss: 38.45386062969919\n",
+      "Validation Accuracy: 0.9132075309753418 auc 0.8367486338797815\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/25 | Train Accuracy: 1.0 | Train Loss: 14.068549347110093\n",
+      "Validation Accuracy: 0.9245283007621765 auc 0.8592896174863389\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 20%|██        | 5/25 [00:05<00:21,  1.05s/it]"
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     {
      "name": "stdout",
      "output_type": "stream",
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-      "Epoch 0/20 | Train Accuracy: 1.0 | Train Loss: 8.364538975722887\n",
-      "Validation Accuracy: 0.7320754528045654 auc 0.1592130100188761\n"
+      "Epoch 4/25 | Train Accuracy: 1.0 | Train Loss: 7.064016109332442\n",
+      "Validation Accuracy: 0.9358490705490112 auc 0.87743950039032\n"
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-      "Validation Accuracy: 0.7320754528045654 auc 0.5462465514737912\n"
+      "Epoch 5/25 | Train Accuracy: 1.0 | Train Loss: 6.148923043161631\n",
+      "Validation Accuracy: 0.9358490705490112 auc 0.882903981264637\n"
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-      "Epoch 2/20 | Train Accuracy: 1.0 | Train Loss: 525.2877785372972\n",
-      "Validation Accuracy: 0.7358490824699402 auc 0.8560331058516045\n"
+      "Epoch 6/25 | Train Accuracy: 1.0 | Train Loss: 6.028049414977431\n",
+      "Validation Accuracy: 0.9358490705490112 auc 0.8889539422326307\n"
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-      "Epoch 3/20 | Train Accuracy: 1.0 | Train Loss: 200.01628349609354\n",
-      "Validation Accuracy: 0.7396226525306702 auc 0.8799186873820241\n"
+      "Epoch 7/25 | Train Accuracy: 1.0 | Train Loss: 5.655132298357785\n",
+      "Validation Accuracy: 0.9358490705490112 auc 0.8924668227946916\n"
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-      "Validation Accuracy: 0.7433962225914001 auc 0.8205314360389139\n"
+      "Epoch 8/25 | Train Accuracy: 1.0 | Train Loss: 5.505190880969167\n",
+      "Validation Accuracy: 0.9433962106704712 auc 0.8950039032006245\n"
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-      "Validation Accuracy: 0.7358490824699402 auc 0.8681573979962247\n"
+      "Epoch 9/25 | Train Accuracy: 1.0 | Train Loss: 5.286113473121077\n",
+      "Validation Accuracy: 0.9396226406097412 auc 0.8955893832943013\n"
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-      "Validation Accuracy: 0.7433962225914001 auc 0.9358211122404531\n"
+      "Epoch 10/25 | Train Accuracy: 1.0 | Train Loss: 5.151115003041923\n",
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-      "Validation Accuracy: 0.7584905624389648 auc 0.9371279221722085\n"
+      "Epoch 11/25 | Train Accuracy: 1.0 | Train Loss: 4.604029227048159\n",
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-      "Validation Accuracy: 0.7547169923782349 auc 0.9406127486568898\n"
+      "Epoch 12/25 | Train Accuracy: 1.0 | Train Loss: 4.592545760795474\n",
+      "Validation Accuracy: 0.947169840335846 auc 0.9080796252927401\n"
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-      "Validation Accuracy: 0.7547169923782349 auc 0.941846958036881\n"
+      "Epoch 13/25 | Train Accuracy: 1.0 | Train Loss: 4.154761942103505\n",
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-      "Validation Accuracy: 0.7547169923782349 auc 0.9493974154203572\n"
+      "Epoch 14/25 | Train Accuracy: 1.0 | Train Loss: 3.5454122377559543\n",
+      "Validation Accuracy: 0.9358490705490112 auc 0.9192037470725994\n"
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-      "Validation Accuracy: 0.7584905624389648 auc 0.9449687817627413\n"
+      "Epoch 15/25 | Train Accuracy: 1.0 | Train Loss: 3.129574440885335\n",
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+      "Epoch 16/25 | Train Accuracy: 1.0 | Train Loss: 3.14078975119628\n",
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-      "Validation Accuracy: 0.7622641324996948 auc 0.9478002032815449\n"
+      "Epoch 17/25 | Train Accuracy: 1.0 | Train Loss: 2.7704373244196177\n",
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+      "Epoch 18/25 | Train Accuracy: 1.0 | Train Loss: 3.2044622113462538\n",
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+      "Epoch 19/25 | Train Accuracy: 1.0 | Train Loss: 2.7323956332402304\n",
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+      "Epoch 20/25 | Train Accuracy: 1.0 | Train Loss: 2.5043672195170075\n",
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+      "Epoch 21/25 | Train Accuracy: 1.0 | Train Loss: 2.120341687972541\n",
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+      "Epoch 22/25 | Train Accuracy: 1.0 | Train Loss: 10.550111314398237\n",
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+      "Epoch 23/25 | Train Accuracy: 1.0 | Train Loss: 12.62315985956775\n",
+      "Validation Accuracy: 0.9207547307014465 auc 0.9137392661982825\n"
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24/25 | Train Accuracy: 1.0 | Train Loss: 14.581160920747408\n",
+      "Validation Accuracy: 0.9396226406097412 auc 0.8467993754879001\n"
      ]
     },
     {
@@ -633,14 +729,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Graph(num_nodes={'client': 861, 'merchant': 482, 'transaction': 12053},\n",
+      "Graph(num_nodes={'client': 861, 'merchant': 482, 'transaction': 12318},\n",
       "      num_edges={('client', 'buy', 'transaction'): 12318, ('merchant', 'sell', 'transaction'): 12318, ('transaction', 'bought', 'client'): 12318, ('transaction', 'issued', 'merchant'): 12318},\n",
       "      metagraph=[('client', 'transaction', 'buy'), ('transaction', 'client', 'bought'), ('transaction', 'merchant', 'issued'), ('merchant', 'transaction', 'sell')])\n"
      ]
@@ -660,14 +756,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final Test Accuracy: 0.7509434223175049 auc 0.9314650791346014\n"
+      "Final Test Accuracy: 0.9245283007621765 auc 0.8122560499609679\n"
      ]
     }
    ],
@@ -706,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -757,7 +853,7 @@
        "              predictor=None, random_state=None, ...)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -778,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -803,14 +899,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final Test Accuracy: 0.8377358490566038 auc 0.9036590678089155\n"
+      "Final Test Accuracy: 0.9283018867924528 auc 0.8677790788446526\n"
      ]
     }
    ],
@@ -846,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -857,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -874,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [