[DOC] Update README Subpages

README.md

@@ -25,12 +25,6 @@
 [RAPIDS](https://rapids.ai) cuGraph GNN is a monorepo containing packages for GPU-accelerated graph neural networks (GNNs).
 cuGraph-GNN supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms.
 
-<div align="center">
-
-[GNN Support](./readme_pages/gnn_support.md)
-
-</div>
-
 -----
 ## News
 

readme_pages/cugraph_pyg.md

@@ -1,22 +1,44 @@
 # cugraph_pyg
 
-[RAPIDS](https://rapids.ai) cugraph_pyg enables the ability to use cugraph Property Graphs with PyTorch Geometric (PyG).  PyG users will have access to cuGraph and cuGraph-Service through the PyG GraphStore, FeatureStore, and Sampler interfaces.  Through cugraph_pyg, PyG users have the full power of cuGraph's GPU-accelerated algorithms for graph analytics, such as sampling, centrality computation, and community detection.
+## Overview
+**cugraph_pyg** brings GPU-accelerated graph processing from [RAPIDS](https://rapids.ai) cuGraph to PyTorch Geometric (PyG), enabling seamless integration of cuGraph's high-performance capabilities into the PyG ecosystem. By providing native implementations of PyG's `GraphStore`, `FeatureStore`, and `Loader` interfaces, cugraph_pyg unlocks powerful GPU-accelerated graph analytics—including neighborhood sampling, centrality metrics, and community detection—directly within your PyG workflows.
 
-
-The goal of `cugraph_pyg` is to enable accelerated single-GPU and multi-node, multi-GPU cugraph accelerated graphs to help train large-scale Graph Neural Networks (GNN) on PyG by providing duck-typed drop-in replacements of the `GraphStore`, `FeatureStore`, and `Sampler` interfaces backed by either cuGraph or cuGraph-Service.
-
-Users of cugraph_pyg have the option of installing either the cugraph or cugraph_service_client packages.  Only one is required.
+Designed for scalability, cugraph_pyg supports both single-GPU and multi-node, multi-GPU configurations, making it ideal for training large-scale Graph Neural Networks (GNNs). Simply use cugraph_pyg as a drop-in replacement for standard PyG components to accelerate your graph learning pipelines with minimal code changes.
 
 ## Usage
 ```
-G = cuGraph.PropertyGraph()
-...
-feature_store, graph_store = to_pyg(G)
-sampler = CuGraphSampler(
-    data=(feature_store, graph_store),
-    shuffle=True,
-    num_neighbors=[10,25],
-    batch_size=50,
+edge_index = torch.tensor([
+    [0, 3, 2, 8, 1, 8, 5, 0, 7, 3],
+    [4, 1, 1, 2, 7, 6, 4, 7, 8, 5]
+])
+x = torch.tensor([
+    [0.6, 0.5, 0.4],
+    [0.0, 0.1, 1.1],
+    [0.1, 0.2, 0.3],
+    [0.5, 0.1, 0.1],
+    [0.8, 0.8, 0.7],
+    [1.1, 0.1, 0.1],
+    [1.2, 0.2, 0.3],
+    [0.2, 0.1, 0.2],
+    [0.3, 0.2, 0.5],
+])
+y = torch.tensor([0, 1, 0, 0, 1, 0, 0, 1, 1])
+
+graph_store = cugraph_pyg.data.GraphStore()
+feature_store = cugraph_pyg.data.FeatureStore()
+
+graph_store[('n', 'to', 'n'), 'coo', False, (9, 9)] = edge_index
+feature_store['n', 'x', None] = x
+feature_store['n', 'y', None] = y
+
+loader = cugraph_pyg.data.NeighborLoader(
+    (feature_store, graph_store),
+    input_nodes=torch.arange(9),
+    num_neighbors=[2, 2],
+    batch_size=2,
 )
+
+for batch in loader:
+    ...
 ...
 ```