[doc] highlight graphbolt (#6745)

docs/source/api/python/dgl.sparse_v0.rst

 .. _apibackend:
 
-🆕 dgl.sparse
+dgl.sparse
 =================================
 
 `dgl.sparse` is a library for sparse operators that are commonly used in GNN models.

docs/source/graphbolt/index.rst

-🆕 Tutorial: GraphBolt
-==========
-
-This tutorial introduces the **graphbolt** module, which is a data loading
-framework for GNN with high flexibility and scalability.
-
-.. toctree::
-  :maxdepth: 2
-  :titlesonly:
-
-  walkthrough.nblink
-  ondisk-dataset

docs/source/graphbolt/walkthrough.nblink

-{
-    "path": "../../../notebooks/graphbolt/walkthrough.ipynb"
-}

docs/source/index.rst

@@ -22,12 +22,11 @@ Welcome to Deep Graph Library Tutorials and Documentation
    :titlesonly:
    :glob:
 
-   graphbolt/index
+   stochastic_training/index
    guide/index
    guide_cn/index
    guide_ko/index
    notebooks/sparse/index
-   notebooks/stochastic_training/index
    tutorials/cpu/index
    tutorials/multi/index
    tutorials/dist/index

docs/source/notebooks/sparse/index.rst

-🆕 Tutorials: dgl.sparse
+Tutorials: dgl.sparse
 =========================
 
 The tutorial set cover the basic usage of DGL's sparse matrix class and operators. You can begin with "Quickstart" and "Building a Graph Convolutional Network Using Sparse Matrices". The rest of the tutorials demonstrate the usage by end-to-end examples. All the tutorials are written in Jupyter Notebook and can be played on Google Colab.

docs/source/notebooks/stochastic_training/index.rst

-Stochastic Training of GNNs
-===========================
-
-This tutorial introduces how to train GNNs with stochastic training.
-
-.. toctree::
-  :maxdepth: 1
-  :titlesonly:
-
-  neighbor_sampling_overview.nblink
-  node_classification.nblink
-  link_prediction.nblink

docs/source/notebooks/stochastic_training/link_prediction.nblink

-{
-    "path": "../../../../notebooks/stochastic_training/link_prediction.ipynb"
-}

docs/source/notebooks/stochastic_training/neighbor_sampling_overview.nblink

-{
-    "path": "../../../../notebooks/stochastic_training/neighbor_sampling_overview.ipynb"
-}

docs/source/notebooks/stochastic_training/node_classification.nblink

-{
-    "path": "../../../../notebooks/stochastic_training/node_classification.ipynb"
-}

docs/source/stochastic_training/index.rst

+🆕 Stochastic Training of GNNs with GraphBolt
+=============================================
+
+GraphBolt is a data loading framework for GNN with high flexibility and
+scalability. It is built on top of DGL and PyTorch.
+
+This tutorial introduces how to enable stochastic training of GNNs with
+GraphBolt.
+
+.. toctree::
+  :maxdepth: 1
+  :titlesonly:
+
+  neighbor_sampling_overview.nblink
+  node_classification.nblink
+  link_prediction.nblink
+  walkthrough.nblink
+  ondisk-dataset

docs/source/stochastic_training/link_prediction.nblink

+{
+    "path": "../../../notebooks/stochastic_training/link_prediction.ipynb"
+}

docs/source/stochastic_training/neighbor_sampling_overview.nblink

+{
+    "path": "../../../notebooks/stochastic_training/neighbor_sampling_overview.ipynb"
+}

docs/source/stochastic_training/node_classification.nblink

+{
+    "path": "../../../notebooks/stochastic_training/node_classification.ipynb"
+}

docs/source/graphbolt/ondisk-dataset.rst → docs/source/stochastic_training/ondisk-dataset.rst

-.. _graphbolt-ondisk-dataset:
+.. _stochastic_training-ondisk-dataset:
 
 Prepare dataset
 ===============

docs/source/stochastic_training/walkthrough.nblink

+{
+    "path": "../../notebooks/graphbolt/walkthrough.ipynb"
+}