diff --git a/federatedscope/trafficflow/dataloader/traffic_dataloader_v2.py b/federatedscope/trafficflow/dataloader/traffic_dataloader_v2.py
index bba354c..84f6df0 100644
--- a/federatedscope/trafficflow/dataloader/traffic_dataloader_v2.py
+++ b/federatedscope/trafficflow/dataloader/traffic_dataloader_v2.py
@@ -188,35 +188,34 @@ def load_traffic_data(config, client_cfgs):
     return data_list, config
 
 
-def split_into_mini_graphs(tensor, minigraph_size):
+def split_into_mini_graphs(tensor, graph_size, dummy_value=0):
     """
     Splits a tensor into mini-graphs of specified size. Pads the last mini-graph with dummy nodes if necessary.
 
     Args:
         tensor (np.ndarray): Input tensor with shape (timestep, horizon, node_num, dim).
-        minigraph_size (int): The size of each mini-graph.
+        graph_size (int): The size of each mini-graph.
+        dummy_value (float, optional): The value to use for dummy nodes. Default is 0.
 
     Returns:
-        np.ndarray: Output tensor with shape (timestep, horizon, minigraph_num, minigraph_size, dim).
+        np.ndarray: Output tensor with shape (timestep, horizon, graph_num, graph_size, dim).
     """
     timestep, horizon, node_num, dim = tensor.shape
 
     # Calculate the number of mini-graphs
-    minigraph_num = (node_num + minigraph_size - 1) // minigraph_size  # Round up division
+    graph_num = (node_num + graph_size - 1) // graph_size  # Round up division
 
-    # Initialize output tensor with zeros (dummy nodes)
-    output = np.zeros((timestep, horizon, minigraph_num, minigraph_size, dim), dtype=tensor.dtype)
+    # Initialize output tensor with dummy values
+    output = np.full((timestep, horizon, graph_size, graph_num, dim), dummy_value, dtype=tensor.dtype)
 
     # Fill in the real data
-    for i in range(minigraph_num):
-        start_idx = i * minigraph_size
-        end_idx = min(start_idx + minigraph_size, node_num)  # Ensure we don't exceed the node number
+    for i in range(graph_num):
+        start_idx = i * graph_size
+        end_idx = min(start_idx + graph_size, node_num)  # Ensure we don't exceed the node number
         slice_size = end_idx - start_idx
 
-        # Assign the data to the corresponding mini-graph
-        output[:, :, i, :slice_size, :] = tensor[:, :, start_idx:end_idx, :]
-
-        # For the remaining part in the mini-graph, it remains as dummy nodes (zeros)
+        # Assign the data to the corresponding mini-graph (adjusted indexing)
+        output[:, :, :slice_size, i, :] = tensor[:, :, start_idx:end_idx, :]
 
     return output
 
diff --git a/scripts/trafficflow_exp_scripts/D4.yaml b/scripts/trafficflow_exp_scripts/D4.yaml
index fe0e8f9..73d9f70 100644
--- a/scripts/trafficflow_exp_scripts/D4.yaml
+++ b/scripts/trafficflow_exp_scripts/D4.yaml
@@ -45,7 +45,7 @@ model:
   use_day: True
   use_week: True
   use_minigraph: True
-  minigraph_size: 5
+  minigraph_size: 3
 train:
   batch_or_epoch: 'epoch'
   local_update_steps: 1