Update dataset.rst

docs/source/tutorial/dataset.rst

@@ -4,12 +4,12 @@ Preparing the Temporal Graph Dataset
 In this tutorial, we will show the preparation process of the temporal graph datase that can be used by StarryGL.
 In this tutorial, we will show the preparation process of the temporal graph datase that can be used by StarryGL.
 
 
 1 Preparing the Temporal Graph Dataset for CTDG
 1 Preparing the Temporal Graph Dataset for CTDG
--------------
+-----------------------------------------------
 
 
 This section writes the steps to prepare the dataset for CTDG.
 This section writes the steps to prepare the dataset for CTDG.
 
 
 1.1 Read Raw Data
 1.1 Read Raw Data
--------------
+-----------------
 
 
 Take Wikipedia dataset as an example, the raw data files are as follows:
 Take Wikipedia dataset as an example, the raw data files are as follows:
 
 
@@ -43,7 +43,7 @@ Here is an example to read the raw data files:
         the number of edges in graph is {}'.format(num_nodes, num_edges))
         the number of edges in graph is {}'.format(num_nodes, num_edges))
 
 
 1.2 Preprocess Data
 1.2 Preprocess Data
----------------
+-------------------
 
 
 After reading the raw data, we need to preprocess the data to get the data format that can be used by StarryGL. The following code shows the preprocessing process:
 After reading the raw data, we need to preprocess the data to get the data format that can be used by StarryGL. The following code shows the preprocessing process:
 
 
@@ -121,12 +121,12 @@ Finally, we can partition the graph and save the data:
                edge_weight_dict=edge_weight_dict)
                edge_weight_dict=edge_weight_dict)
 
 
 2 Preparing the Temporal Graph Dataset for DTDG
 2 Preparing the Temporal Graph Dataset for DTDG
--------------
+-----------------------------------------------
 
 
 This section writes the steps to prepare the dataset for DTDG.
 This section writes the steps to prepare the dataset for DTDG.
 
 
 2.1 Processing the raw data
 2.1 Processing the raw data
--------------
+---------------------------
 Take elliptic dataset as an example, the raw data files are as follows:
 Take elliptic dataset as an example, the raw data files are as follows:
 
 
 - `elliptic_txs_features.csv`: the node features of the graph
 - `elliptic_txs_features.csv`: the node features of the graph
@@ -173,7 +173,7 @@ This dataset is then called elliptic_temporal.The process of getting the most im
     edgelist_timed.to_csv('elliptic_txs_edgelist_timed.csv', index=False)
     edgelist_timed.to_csv('elliptic_txs_edgelist_timed.csv', index=False)
 
 
 2.2 Read raw data and preprocess Data
 2.2 Read raw data and preprocess Data
----------------
+-------------------------------------
 After the previous step, we will read in our dataset and use a separate wrapped class to process the corresponding data:
 After the previous step, we will read in our dataset and use a separate wrapped class to process the corresponding data:
 
 
 .. code-block:: python
 .. code-block:: python
@@ -231,7 +231,7 @@ We construct a wrapped Elliptic_Temporal_Dataset object to store the data. The d
 - `max_degree`: a tensor that stores the maximum out-degree over all time steps in the dataset.
 - `max_degree`: a tensor that stores the maximum out-degree over all time steps in the dataset.
 
 
 2.3 Generate a graph from graph data
 2.3 Generate a graph from graph data
----------------
+------------------------------------
 In order to facilitate further processing later, the corresponding graph is generated from the graph dataset using the encapsulated function:
 In order to facilitate further processing later, the corresponding graph is generated from the graph dataset using the encapsulated function:
 
 
 .. code-block:: python
 .. code-block:: python