Added Centrality Algorithms Example Jupyter Notebook

examples/centrality-algorithms.ipynb

@@ -109,7 +109,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we load this data (nodes and edges) into a Graph Database and a GDS graph."
+    "Since this graph is very small, a straight-forward Cypher `UNWIND` query is the simplest way to create our graph in the database.\n",
+    "\n",
+    "Larger graphs may need a more sophisticated importing technique like batching, `neo4j-admin import` or Arrow `CREATE DATABASE`."
    ]
   },
   {
@@ -145,7 +147,8 @@
     "\n",
     "# We can use convenience methods on `G` to check if the projection looks correct\n",
     "print(f\"Graph '{G.name()}' node count: {G.node_count()}\")\n",
-    "print(f\"Graph '{G.name()}' node labels: {G.node_labels()}\")"
+    "print(f\"Graph '{G.name()}' node labels: {G.node_labels()}\")\n",
+    "print(f\"Graph '{G.name()}' relationship count: {G.relationship_count()}\")"
    ]
   },
   {
@@ -215,6 +218,13 @@
     "eigenvector_centrality_result.centralityDistribution"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now write our results back to the database."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -302,6 +312,13 @@
     "betweenness_centrality_result.centralityDistribution"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now write our results back to the database."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -373,6 +390,13 @@
     "degree_centrality_result.centralityDistribution"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now write our results back to the database."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -438,13 +462,29 @@
     "- For the city metadata (metropolitan population, latitude, and longitude):\n",
     "Austin R. Benson, David F. Gleich, and Jure Leskovec. \"Higher-order Organization of Complex Networks.\" Science, 353.6295 (2016): 163–166.\n",
     "\n",
+    "- Link to the dataset: https://snap.stanford.edu/data/reachability.html\n",
+    "\n",
     "- Notebook contributed by [Kedar Ghule](https://github.com/kedarghule)"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,