Merge pull request neo4j#175 from adamnsch/export-gnn-example

Add GCN on CORA example notebook

Author: adamnsch

README.md

@@ -37,6 +37,7 @@ For extensive documentation of all capabilities, please refer to the [GDS Python
 Extensive end-to-end examples in Jupyter ready-to-run notebooks can be found in the [`examples` source directory](https://github.com/neo4j/graph-data-science-client/tree/main/examples):
 
 * [Computing similarities with kNN based on FastRP embeddings](https://github.com/neo4j/graph-data-science-client/tree/main/examples/fastrp-and-knn.ipynb)
+* [Exporting from GDS and running GCN with PyG](https://github.com/neo4j/graph-data-science-client/tree/main/examples/import-sample-export-gnn.ipynb)
 
 
 ### Imports and setup

examples/fastrp-and-knn.ipynb

@@ -5,7 +5,7 @@
  "id": "5cb64c55",
  "metadata": {},
  "source": [
- "# graphdatascience example\n",
+ "# FastRP+kNN end-to-end example\n",
  "\n",
  "This notebook exemplifies how to use the `graphdatascience` Python library to operate Neo4j GDS.\n",
  "It runs the FastRP+kNN end-to-end example from the GDS Manual, found [here](https://neo4j.com/docs/graph-data-science/current/end-to-end-examples/fastrp-knn-example).\n",
@@ -16,7 +16,7 @@
  "Running this notebook requires a localhost Neo4j server with a recent GDS version (2.0+) installed.\n",
  "We recommend using Neo4j Desktop.\n",
  "\n",
- "Also required are the Python libraries neo4j ([Neo4j Python Driver](https://neo4j.com/docs/api/python-driver/current/index.html)) and of course the `graphdatascience` (see README for installation instructions)."
+ "Also required are of course the `graphdatascience` Python library (see README for installation instructions)."
  ]
  },
  {
@@ -214,7 +214,7 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3.8.10 64-bit",
  "language": "python",
  "name": "python3"
  },
@@ -229,8 +229,13 @@
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": "3.8.10"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+ }
  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}