Add neo4j_viz block for exampels

Author: FlorentinD

doc/modules/ROOT/pages/tutorials/graph-analytics-serverless-self-managed.adoc

@@ -35,7 +35,7 @@ version `1.15` or later.
 
 [source, python, role=no-test]
 ----
-%pip install "graphdatascience>=1.15" python-dotenv
+%pip install "graphdatascience>=1.15" python-dotenv "neo4j_viz[gds]"
 ----
 
 [source, python, role=no-test]
@@ -252,6 +252,18 @@ G, result = gds.graph.project(
 str(G)
 ----
 
+[source, python, role=no-test]
+----
+# Let us visualize the projected graph
+from neo4j_viz.gds import from_gds
+
+VG = from_gds(gds, G, db_node_properties=["name"])
+for node in VG.nodes:
+    node.caption = node.properties.get("name")
+
+VG.render(initial_zoom=1.2)
+----
+
 == Running Algorithms
 
 You can run algorithms on the constructed graph using the standard GDS

doc/modules/ROOT/pages/tutorials/graph-analytics-serverless-standalone.adoc

@@ -34,7 +34,7 @@ version `1.15` or later.
 
 [source, python, role=no-test]
 ----
-%pip install "graphdatascience>=1.15" python-dotenv
+%pip install "graphdatascience>=1.15" python-dotenv "neo4j_viz[gds]"
 ----
 
 [source, python, role=no-test]
@@ -204,6 +204,18 @@ G = gds.graph.construct("people-fruits", nodes, relationships)
 str(G)
 ----
 
+[source, python, role=no-test]
+----
+# Let us visualize the projected graph
+from neo4j_viz.gds import from_gds
+
+VG = from_gds(gds, G, db_node_properties=["name"])
+for node in VG.nodes:
+    node.caption = node.properties.get("name")
+
+VG.render(initial_zoom=1.2)
+----
+
 == Running Algorithms
 
 You can run algorithms on the constructed graph using the standard GDS

doc/modules/ROOT/pages/tutorials/graph-analytics-serverless.adoc

@@ -35,7 +35,7 @@ version `1.15` or later.
 
 [source, python, role=no-test]
 ----
-%pip install "graphdatascience>=1.15" python-dotenv
+%pip install "graphdatascience>=1.15" python-dotenv "neo4j_viz[gds]"
 ----
 
 [source, python, role=no-test]
@@ -244,6 +244,28 @@ G, result = gds.graph.project(
 str(G)
 ----
 
+[source, python, role=no-test]
+----
+# Let us visualize the projected graph
+from neo4j_viz.gds import from_gds
+
+VG = from_gds(gds, G, db_node_properties=["name"])
+for node in VG.nodes:
+    node.caption = node.properties.get("name")
+
+VG.render(initial_zoom=1.2)
+----
+
+[source, python, role=no-test]
+----
+
+----
+
+[source, python, role=no-test]
+----
+
+----
+
 == Running Algorithms
 
 You can run algorithms on the constructed graph using the standard GDS

examples/graph-analytics-serverless-self-managed.ipynb

@@ -57,7 +57,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install \"graphdatascience>=1.15\" python-dotenv"
+    "%pip install \"graphdatascience>=1.15\" python-dotenv \"neo4j_viz[gds]\""
    ]
   },
   {
@@ -314,6 +314,22 @@
     "str(G)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let us visualize the projected graph\n",
+    "from neo4j_viz.gds import from_gds\n",
+    "\n",
+    "VG = from_gds(gds, G, db_node_properties=[\"name\"])\n",
+    "for node in VG.nodes:\n",
+    "    node.caption = node.properties.get(\"name\")\n",
+    "\n",
+    "VG.render(initial_zoom=1.2)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

examples/graph-analytics-serverless-standalone.ipynb

@@ -58,7 +58,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install \"graphdatascience>=1.15\" python-dotenv"
+    "%pip install \"graphdatascience>=1.15\" python-dotenv \"neo4j_viz[gds]\""
    ]
   },
   {
@@ -272,6 +272,22 @@
     "str(G)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let us visualize the projected graph\n",
+    "from neo4j_viz.gds import from_gds\n",
+    "\n",
+    "VG = from_gds(gds, G, db_node_properties=[\"name\"])\n",
+    "for node in VG.nodes:\n",
+    "    node.caption = node.properties.get(\"name\")\n",
+    "\n",
+    "VG.render(initial_zoom=1.2)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

examples/graph-analytics-serverless.ipynb

@@ -57,7 +57,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install \"graphdatascience>=1.15\" python-dotenv"
+    "%pip install \"graphdatascience>=1.15\" python-dotenv \"neo4j_viz[gds]\""
    ]
   },
   {
@@ -306,6 +306,36 @@
     "str(G)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let us visualize the projected graph\n",
+    "from neo4j_viz.gds import from_gds\n",
+    "\n",
+    "VG = from_gds(gds, G, db_node_properties=[\"name\"])\n",
+    "for node in VG.nodes:\n",
+    "    node.caption = node.properties.get(\"name\")\n",
+    "\n",
+    "VG.render(initial_zoom=1.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},