Refine docs: unify file name in en and zh_CN

alibaba · Sep 3, 2021 · 0f5c454 · 0f5c454
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diff --git a/README.md b/README.md
@@ -19,7 +19,7 @@ Graph-Learn provides Python and C++ interfaces for graph sampling operations, an
 pip install graph-learn
 ```
 
-2. [Build from source](docs/en/install_en.md)
+2. [Build from source](docs/en/install.md)
 
 3. Use Docker
 

diff --git a/README_cn.md b/README_cn.md
@@ -18,7 +18,7 @@ Graph-Learn提供了图采样操作的Python和C++接口，并且提供了一个
 pip install graph-learn
 ```
 
-2. [从源码编译](docs/zh_CN/install_cn.md)
+2. [从源码编译](docs/zh_CN/install.md)
 
 3. 使用Docker
 

diff --git a/docs/en/algo/gnn_programming_model_en.md → docs/en/algo/gnn_programming_model.md b/docs/en/algo/gnn_programming_model_en.md → docs/en/algo/gnn_programming_model.md
diff --git a/docs/en/algo/index.rst b/docs/en/algo/index.rst
@@ -6,6 +6,6 @@ GNN Models
    :maxdepth: 2
    :caption: Contents:
 
-   gnn_programming_model_en
+   gnn_programming_model
    tf/index
    torch/index
diff --git a/docs/en/algo/tf/index.rst b/docs/en/algo/tf/index.rst
@@ -6,10 +6,10 @@ Tensorflow
    :maxdepth: 2
    :caption: Contents:
 
-   tf_data_layer_en
-   tf_model_layer_en
-   tf_loss_en
-   tf_config_en
-   tf_model_example_en
+   tf_data_layer
+   tf_model_layer
+   tf_loss
+   tf_config
+   tf_model_example
    k8s
-   tf_custom_model_tutorial_en
+   tf_custom_model_tutorial
diff --git a/docs/en/algo/tf/tf_config_en.md → docs/en/algo/tf/tf_config.md b/docs/en/algo/tf/tf_config_en.md → docs/en/algo/tf/tf_config.md
diff --git a/...en/algo/tf/tf_custom_model_tutorial_en.md → docs/en/algo/tf/tf_custom_model_tutorial.md b/...en/algo/tf/tf_custom_model_tutorial_en.md → docs/en/algo/tf/tf_custom_model_tutorial.md
diff --git a/docs/en/algo/tf/tf_data_layer_en.md → docs/en/algo/tf/tf_data_layer.md b/docs/en/algo/tf/tf_data_layer_en.md → docs/en/algo/tf/tf_data_layer.md
diff --git a/docs/en/algo/tf/tf_loss_en.md → docs/en/algo/tf/tf_loss.md b/docs/en/algo/tf/tf_loss_en.md → docs/en/algo/tf/tf_loss.md
diff --git a/docs/en/algo/tf/tf_model_example_en.md → docs/en/algo/tf/tf_model_example.md b/docs/en/algo/tf/tf_model_example_en.md → docs/en/algo/tf/tf_model_example.md
diff --git a/docs/en/algo/tf/tf_model_layer_en.md → docs/en/algo/tf/tf_model_layer.md b/docs/en/algo/tf/tf_model_layer_en.md → docs/en/algo/tf/tf_model_layer.md
diff --git a/docs/en/algo/torch/index.rst b/docs/en/algo/torch/index.rst
@@ -6,4 +6,4 @@ PyTorch
    :maxdepth: 2
    :caption: Contents:
 
-   torch_custom_model_tutorial_en
+   torch_custom_model_tutorial
diff --git a/...o/torch/torch_custom_model_tutorial_en.md → ...algo/torch/torch_custom_model_tutorial.md b/...o/torch/torch_custom_model_tutorial_en.md → ...algo/torch/torch_custom_model_tutorial.md
diff --git a/docs/en/global_config_en.md → docs/en/global_config.md b/docs/en/global_config_en.md → docs/en/global_config.md
diff --git a/docs/en/graph/data_loader_en.md → docs/en/graph/data_loader.md b/docs/en/graph/data_loader_en.md → docs/en/graph/data_loader.md
@@ -151,7 +151,7 @@ src_id:int64 dst_id:int64 weight:float feature:string
 0 9 0.097545 yellow:0:s2:10:0.1:0.5
 ```
 
-<br />With a local file as the data source, you can use the file path directly in the script. See the next chapter "[graph object](graph_object_en.md)" for details. <br />
+<br />With a local file as the data source, you can use the file path directly in the script. See the next chapter "[graph object](graph_object.md)" for details. <br />
 
 
 ## User API
@@ -289,7 +289,7 @@ user_node_decoder = gl.Decoder(attr_types=["float", "float", "float"])
 edge_decoder = gl.Decoder(weighted=True)
 ```
 
-<br />After constructing Decoder for each data source, add the data source to the graph and specify the corresponding Decoder, see [graph object](graph_object_en.md) for details.
+<br />After constructing Decoder for each data source, add the data source to the graph and specify the corresponding Decoder, see [graph object](graph_object.md) for details.
 
 ### Decoder and NN models combined
 #### attr_types, attr_dims

diff --git a/docs/en/graph/data_object_en.md → docs/en/graph/data_object.md b/docs/en/graph/data_object_en.md → docs/en/graph/data_object.md
diff --git a/docs/en/graph/graph_object_en.md → docs/en/graph/graph_object.md b/docs/en/graph/graph_object_en.md → docs/en/graph/graph_object.md
diff --git a/docs/en/graph/graph_operator_en.md → docs/en/graph/graph_operator.md b/docs/en/graph/graph_operator_en.md → docs/en/graph/graph_operator.md
diff --git a/...en/graph/graph_operator/graph_query_en.md → docs/en/graph/graph_operator/graph_query.md b/...en/graph/graph_operator/graph_query_en.md → docs/en/graph/graph_operator/graph_query.md
diff --git a/...graph/graph_operator/graph_sampling_en.md → ...en/graph/graph_operator/graph_sampling.md b/...graph/graph_operator/graph_sampling_en.md → ...en/graph/graph_operator/graph_sampling.md
@@ -87,7 +87,7 @@ GL currently has support for the following sampling strategies, corresponding to
 | random | random_with_replacement |
 | topk | Return the neighbors with edge weight topK, if there are not enough neighbors, refer to the padding rule.
 | in_degree | Probability sampling by vertex degree.
-| full | Returns all neighbors, the expand_factor parameter does not work, the result object is SparseNodes or SparseEdges, see "[graph query](graph_query_en.md)" for the object description.
+| full | Returns all neighbors, the expand_factor parameter does not work, the result object is SparseNodes or SparseEdges, see "[graph query](graph_query.md)" for the object description.
 
 
 <br />Padding rules: When there is not enough data for the sampling request, the returned result needs to be filled in some way. By default, `default_neighbor_id` is used to fill in the insufficient `id`. `default_neighbor_id` defaults to 0 and can be set by `gl.set_default_neighbor_id(xx)`. To loop padding, i.e. to loop existing neighbor ids instead of `default_neighbor_id`, set the padding mode `gl.CIRCULAR`, `gl.set_padding_mode(gl.CIRCULAR)`.
diff --git a/...graph/graph_operator/graph_traverse_en.md → ...en/graph/graph_operator/graph_traverse.md b/...graph/graph_operator/graph_traverse_en.md → ...en/graph/graph_operator/graph_traverse.md
@@ -32,7 +32,7 @@ Return:
 """
 ```
 
-<br />Get specific values such as id, weight, attribute, etc. from `Nodes` object, refer to [API](graph_query_en.md). In GSL, vertex traversal reference `g.V()`. <br />
+<br />Get specific values such as id, weight, attribute, etc. from `Nodes` object, refer to [API](graph_query.md). In GSL, vertex traversal reference `g.V()`. <br />
 
 #### Example
 
@@ -113,7 +113,7 @@ Return:
 """
 ```
 
-<br />Get specific values such as id, weight, attribute, etc. from `Edges` object, refer to [API](graph_query_en.md). In GSL, the edge traversal reference is `g.E()`. <br />
+<br />Get specific values such as id, weight, attribute, etc. from `Edges` object, refer to [API](graph_query.md). In GSL, the edge traversal reference is `g.E()`. <br />
 
 #### Example<br />
 

diff --git a/docs/en/graph/graph_operator/index.rst b/docs/en/graph/graph_operator/index.rst
@@ -6,7 +6,7 @@ Graph Operators
    :maxdepth: 2
    :caption: Contents:
 
-   graph_query_en
-   graph_traverse_en
-   graph_sampling_en
-   negative_sampling_en
+   graph_query
+   graph_traverse
+   graph_sampling
+   negative_sampling
diff --git a/...ph/graph_operator/negative_sampling_en.md → ...graph/graph_operator/negative_sampling.md b/...ph/graph_operator/negative_sampling_en.md → ...graph/graph_operator/negative_sampling.md
@@ -10,7 +10,7 @@ The negative sampling operator takes an edge or vertex type as input. When edge
 
 - Define the negative sampling operator by `g.negative_sampler()` to obtain the `NegativeSampler` object `S`.
 - call `S.get(ids)`, to get the `Nodes` object.
-- call the [interface](graph_query_en.md) of the `Nodes` object to get the specific values.
+- call the [interface](graph_query.md) of the `Nodes` object to get the specific values.
 
 
 ```python

diff --git a/docs/en/graph/gsl_en.md → docs/en/graph/gsl.md b/docs/en/graph/gsl_en.md → docs/en/graph/gsl.md
@@ -487,7 +487,7 @@ The result of GSL execution is an object consisting of all wandering vertices or
 
 
 ### datasets
-**graphlearn.Dataset** interface for constructing the result of a Query as a Numpy composed **graphlearn.Nodes**/**graphlearn.Edges** or **graphlearn. Edges** object generator. see the documentation [data_object](data_object_cn.md) for a description of objects such as Nodes.
+**graphlearn.Dataset** interface for constructing the result of a Query as a Numpy composed **graphlearn.Nodes**/**graphlearn.Edges** or **graphlearn. Edges** object generator. see the documentation [data_object](data_object.md) for a description of objects such as Nodes.
 
 ```python
 class Dataset:

diff --git a/docs/en/graph/index.rst b/docs/en/graph/index.rst
@@ -6,9 +6,9 @@ Graph
    :maxdepth: 2
    :caption: Contents:
 
-   data_loader_en
-   graph_object_en
-   data_object_en
-   graph_operator_en
+   data_loader
+   graph_object
+   data_object
+   graph_operator
    graph_operator/index
-   gsl_en
+   gsl
diff --git a/docs/en/index.rst b/docs/en/index.rst
@@ -11,11 +11,11 @@ Welcome to GraphLearn's documentation!
    :caption: Contents:
 
 
-   install_cn
-   quick_start_cn
+   install
+   quick_start
    graph/index
    algo/index
-   global_config_cn
+   global_config
    operator
    other_source
 
diff --git a/docs/en/install_en.md → docs/en/install.md b/docs/en/install_en.md → docs/en/install.md
diff --git a/docs/en/other_source.md b/docs/en/other_source.md
@@ -39,7 +39,7 @@ A `Record` contains several columns described by **Schema**.
 Each column value may be in type of `Int32`, `Int64`, `Float`, `Double` or `String`.
 
 The raw graph data will be placed in **StructuredAccessFile**s, **GL** will load it and build graph index.
-Refer [HERE](graph/data_loader_en.md) for more supported raw data formats.
+Refer [HERE](graph/data_loader.md) for more supported raw data formats.
 
 The **GetRecordCount()** interface in file system should return the size in records for a **StructuredAccessFile**.
 
@@ -61,4 +61,4 @@ Each file system should has a unique scheme, such as ```hdfs://```.
 REGISTER_FILE_SYSTEM("scheme", MyFileSystem);
 ```
 
-After registration, you can use such a file path when building [Graph](graph/graph_object_en.md).
+After registration, you can use such a file path when building [Graph](graph/graph_object.md).
diff --git a/docs/en/quick_start_en.md → docs/en/quick_start.md b/docs/en/quick_start_en.md → docs/en/quick_start.md
@@ -57,7 +57,7 @@ git clone https://github.com/alibaba/graph-learn.git
 git submodule update --init
 ```
 
-Refer to [building from source](install_en.md) to build pass.
+Refer to [building from source](install.md) to build pass.
 
 ### Implement your operator class
 
@@ -109,6 +109,6 @@ For example, if a new sampler named **xxxSampler**, you can call it like this:
 g.sample(count).by("xxx")...
 ```
 
-More information about **API** refer to [this](graph/gsl_en.md).
+More information about **API** refer to [this](graph/gsl.md).
 
 
diff --git a/docs/zh_CN/algo/gnn_programming_model_cn.md → docs/zh_CN/algo/gnn_programming_model.md b/docs/zh_CN/algo/gnn_programming_model_cn.md → docs/zh_CN/algo/gnn_programming_model.md
diff --git a/docs/zh_CN/algo/index.rst b/docs/zh_CN/algo/index.rst
@@ -6,6 +6,6 @@ GNN模型开发
    :maxdepth: 2
    :caption: Contents:
 
-   gnn_programming_model_cn
+   gnn_programming_model
    tf/index
    torch/index
diff --git a/docs/zh_CN/algo/tf/index.rst b/docs/zh_CN/algo/tf/index.rst
@@ -6,10 +6,10 @@ Tensorflow
    :maxdepth: 2
    :caption: Contents:
 
-   tf_data_layer_cn
-   tf_model_layer_cn
-   tf_loss_cn
-   tf_config_cn
-   tf_model_example_cn
+   tf_data_layer
+   tf_model_layer
+   tf_loss
+   tf_config
+   tf_model_example
    k8s
-   tf_custom_model_tutorial_cn
+   tf_custom_model_tutorial
diff --git a/docs/zh_CN/algo/tf/tf_config_cn.md → docs/zh_CN/algo/tf/tf_config.md b/docs/zh_CN/algo/tf/tf_config_cn.md → docs/zh_CN/algo/tf/tf_config.md
diff --git a/...CN/algo/tf/tf_custom_model_tutorial_cn.md → ...zh_CN/algo/tf/tf_custom_model_tutorial.md b/...CN/algo/tf/tf_custom_model_tutorial_cn.md → ...zh_CN/algo/tf/tf_custom_model_tutorial.md
diff --git a/docs/zh_CN/algo/tf/tf_data_layer_cn.md → docs/zh_CN/algo/tf/tf_data_layer.md b/docs/zh_CN/algo/tf/tf_data_layer_cn.md → docs/zh_CN/algo/tf/tf_data_layer.md
diff --git a/docs/zh_CN/algo/tf/tf_loss_cn.md → docs/zh_CN/algo/tf/tf_loss.md b/docs/zh_CN/algo/tf/tf_loss_cn.md → docs/zh_CN/algo/tf/tf_loss.md
diff --git a/docs/zh_CN/algo/tf/tf_model_example_cn.md → docs/zh_CN/algo/tf/tf_model_example.md b/docs/zh_CN/algo/tf/tf_model_example_cn.md → docs/zh_CN/algo/tf/tf_model_example.md
diff --git a/docs/zh_CN/algo/tf/tf_model_layer_cn.md → docs/zh_CN/algo/tf/tf_model_layer.md b/docs/zh_CN/algo/tf/tf_model_layer_cn.md → docs/zh_CN/algo/tf/tf_model_layer.md
diff --git a/docs/zh_CN/algo/torch/index.rst b/docs/zh_CN/algo/torch/index.rst
@@ -6,4 +6,4 @@ PyTorch
    :maxdepth: 2
    :caption: Contents:
 
-   torch_custom_model_tutorial_cn
+   torch_custom_model_tutorial
diff --git a/...o/torch/torch_custom_model_tutorial_cn.md → ...algo/torch/torch_custom_model_tutorial.md b/...o/torch/torch_custom_model_tutorial_cn.md → ...algo/torch/torch_custom_model_tutorial.md
diff --git a/docs/zh_CN/global_config_cn.md → docs/zh_CN/global_config.md b/docs/zh_CN/global_config_cn.md → docs/zh_CN/global_config.md
diff --git a/docs/zh_CN/graph/data_loader_cn.md → docs/zh_CN/graph/data_loader.md b/docs/zh_CN/graph/data_loader_cn.md → docs/zh_CN/graph/data_loader.md
@@ -152,7 +152,7 @@ src_id:int64  dst_id:int64  weight:float  feature:string
 0 9 0.097545  yellow:0:s2:10:0.1:0.5
 ```
 
-<br />通过本地文件作为数据源，可以直接在脚本中使用文件路径。详见下一章“[图对象](graph_object_cn.md)”。<br />
+<br />通过本地文件作为数据源，可以直接在脚本中使用文件路径。详见下一章“[图对象](graph_object.md)”。<br />
 
 <a name="mzVG6"></a>
 
@@ -294,7 +294,7 @@ user_node_decoder = gl.Decoder(attr_types=["float", "float", "float"])
 edge_decoder = gl.Decoder(weighted=True)
 ```
 
-<br />对每一个数据源构建完Decoder之后，在图中加入数据源，并指定对应的Decoder，详见[图对象](graph_object_cn.md) 。
+<br />对每一个数据源构建完Decoder之后，在图中加入数据源，并指定对应的Decoder，详见[图对象](graph_object.md) 。
 
 ### Decoder和NN模型结合
 #### attr_types, attr_dims

diff --git a/docs/zh_CN/graph/data_object_cn.md → docs/zh_CN/graph/data_object.md b/docs/zh_CN/graph/data_object_cn.md → docs/zh_CN/graph/data_object.md
diff --git a/docs/zh_CN/graph/graph_object_cn.md → docs/zh_CN/graph/graph_object.md b/docs/zh_CN/graph/graph_object_cn.md → docs/zh_CN/graph/graph_object.md
diff --git a/docs/zh_CN/graph/graph_operator_cn.md → docs/zh_CN/graph/graph_operator.md b/docs/zh_CN/graph/graph_operator_cn.md → docs/zh_CN/graph/graph_operator.md
diff --git a/...CN/graph/graph_operator/graph_query_cn.md → ...zh_CN/graph/graph_operator/graph_query.md b/...CN/graph/graph_operator/graph_query_cn.md → ...zh_CN/graph/graph_operator/graph_query.md
diff --git a/...graph/graph_operator/graph_sampling_cn.md → ...CN/graph/graph_operator/graph_sampling.md b/...graph/graph_operator/graph_sampling_cn.md → ...CN/graph/graph_operator/graph_sampling.md
@@ -95,7 +95,7 @@ GL目前已支持以下几种采样策略，对应产生`NeighborSampler`对象
 | random_without_replacement | 无放回随机采样，邻居数不够时，参考填充规则 |
 | topk | 返回边权重topK的邻居，邻居数不够时，参考填充规则 |
 | in_degree | 以顶点入度为概率采样 |
-| full | 返回全部的邻居，此时expand_factor参数不起作用，结果对象为SparseNodes或SparseEdges，对象描述详见“[数据查询](graph_query_cn.md#FPU74)” |
+| full | 返回全部的邻居，此时expand_factor参数不起作用，结果对象为SparseNodes或SparseEdges，对象描述详见“[数据查询](graph_query.md#FPU74)” |
 
 
 <br />填充规则：当采样要求的数据不足时，需要以某种方式填充返回结果。默认情况下，用`default_neighbor_id`填充补齐不足的`id`，`default_neighbor_id`默认为0，可以通过`gl.set_default_neighbor_id(xx)`来设置。如要循环填充，即循环使用已有邻居id而非`default_neighbor_id`，则设置填充模式`gl.CIRCULAR`，`gl.set_padding_mode(gl.CIRCULAR)`。

diff --git a/...graph/graph_operator/graph_traverse_cn.md → ...CN/graph/graph_operator/graph_traverse.md b/...graph/graph_operator/graph_traverse_cn.md → ...CN/graph/graph_operator/graph_traverse.md
@@ -34,7 +34,7 @@ Return:
 """
 ```
 
-<br />通过`Nodes`对象获取具体的值，如id、weight、attribute等，参考[API](graph_query_cn.md)。在GSL中，顶点遍历参考`g.V()`。<br />
+<br />通过`Nodes`对象获取具体的值，如id、weight、attribute等，参考[API](graph_query.md)。在GSL中，顶点遍历参考`g.V()`。<br />
 
 <a name="aNB50"></a>
 #### 示例
@@ -117,7 +117,7 @@ Return:
 """
 ```
 
-<br />通过`Edges`对象获取具体的值，如id、weight、attribute等，参考[API](graph_query_cn.md#FPU74)。在GSL中，边遍历参考`g.E()`。<br />
+<br />通过`Edges`对象获取具体的值，如id、weight、attribute等，参考[API](graph_query.md#FPU74)。在GSL中，边遍历参考`g.E()`。<br />
 
 <a name="RVPmZ"></a>
 #### 示例<br />

diff --git a/docs/zh_CN/graph/graph_operator/index.rst b/docs/zh_CN/graph/graph_operator/index.rst
@@ -6,7 +6,7 @@
    :maxdepth: 2
    :caption: Contents:
 
-   graph_query_cn
-   graph_traverse_cn
-   graph_sampling_cn
-   negative_sampling_cn
+   graph_query
+   graph_traverse
+   graph_sampling
+   negative_sampling
diff --git a/...ph/graph_operator/negative_sampling_cn.md → ...graph/graph_operator/negative_sampling.md b/...ph/graph_operator/negative_sampling_cn.md → ...graph/graph_operator/negative_sampling.md
@@ -12,7 +12,7 @@
 
 - 通过`g.negative_sampler()`定义负采样算子，得到`NegativeSampler`对象`S`；
 - 调用`S.get(ids)`, 得到`Nodes`对象；
-- 调用`Nodes`对象的[接口](graph_query_cn.md#FPU74)获取具体的值；
+- 调用`Nodes`对象的[接口](graph_query.md#FPU74)获取具体的值；
 
 
 

diff --git a/docs/zh_CN/graph/gsl_cn.md → docs/zh_CN/graph/gsl.md b/docs/zh_CN/graph/gsl_cn.md → docs/zh_CN/graph/gsl.md
@@ -494,7 +494,7 @@ GSL执行的结果是Query中描述的所有游走的顶点或边构成的对象
 
 
 ### Dataset
-**graphlearn.Dataset**接口，用于将Query的结果构造为Numpy组成的**graphlearn.Nodes**/**graphlearn.Edges**或**graphlearn.SparseNodes**/**graphlearn.Edges**对象的生成器。Nodes等对象描述详见文档 [数据对象](data_object_cn.md)
+**graphlearn.Dataset**接口，用于将Query的结果构造为Numpy组成的**graphlearn.Nodes**/**graphlearn.Edges**或**graphlearn.SparseNodes**/**graphlearn.Edges**对象的生成器。Nodes等对象描述详见文档 [数据对象](data_object.md)
 
 ```python
 class Dataset:

diff --git a/docs/zh_CN/graph/index.rst b/docs/zh_CN/graph/index.rst
@@ -6,9 +6,9 @@
    :maxdepth: 2
    :caption: Contents:
 
-   data_loader_cn
-   graph_object_cn
-   data_object_cn
-   graph_operator_cn
+   data_loader
+   graph_object
+   data_object
+   graph_operator
    graph_operator/index
-   gsl_cn
+   gsl
diff --git a/docs/zh_CN/index.rst b/docs/zh_CN/index.rst
@@ -11,11 +11,11 @@ Welcome to GraphLearn's documentation!
    :caption: Contents:
 
 
-   install_cn
-   quick_start_cn
+   install
+   quick_start
    graph/index
    algo/index
-   global_config_cn
+   global_config
    operator
    other_source
 
diff --git a/docs/zh_CN/install_cn.md → docs/zh_CN/install.md b/docs/zh_CN/install_cn.md → docs/zh_CN/install.md
diff --git a/docs/zh_CN/operator.md b/docs/zh_CN/operator.md
@@ -57,7 +57,7 @@ git clone https://github.com/alibaba/graph-learn.git
 git submodule update --init
 ```
 
-Refer to [building from source](install_cn.md) to build pass.
+Refer to [building from source](install.md) to build pass.
 
 ### Implement your operator class
 
@@ -109,6 +109,6 @@ For example, if a new sampler named **xxxSampler**, you can call it like this:
 g.sample(count).by("xxx")...
 ```
 
-More information about **API** refer to [this](graph/gsl_cn.md).
+More information about **API** refer to [this](graph/gsl.md).
 
 
diff --git a/docs/zh_CN/other_source.md b/docs/zh_CN/other_source.md
@@ -39,7 +39,7 @@ A `Record` contains several columns described by **Schema**.
 Each column value may be in type of `Int32`, `Int64`, `Float`, `Double` or `String`.
 
 The raw graph data will be placed in **StructuredAccessFile**s, **GL** will load it and build graph index.
-Refer [HERE](graph/data_loader_cn.md) for more supported raw data formats.
+Refer [HERE](graph/data_loader.md) for more supported raw data formats.
 
 The **GetRecordCount()** interface in file system should return the size in records for a **StructuredAccessFile**.
 
@@ -61,4 +61,4 @@ Each file system should has a unique scheme, such as ```hdfs://```.
 REGISTER_FILE_SYSTEM("scheme", MyFileSystem);
 ```
 
-After registration, you can use such a file path when building [Graph](graph/graph_object_cn.md).
+After registration, you can use such a file path when building [Graph](graph/graph_object.md).
diff --git a/docs/zh_CN/quick_start_cn.md → docs/zh_CN/quick_start.md b/docs/zh_CN/quick_start_cn.md → docs/zh_CN/quick_start.md
@@ -217,7 +217,7 @@ def load_graph(args):
   return g
 
 
-# 调用.init()进行初始化。这里以单机运行为例，分布式详见[图对象-初始化数据](graph_object_cn.md)。
+# 调用.init()进行初始化。这里以单机运行为例，分布式详见[图对象-初始化数据](graph_object.md)。
 g.init()
 ```