Address review comments

Co-Authored-By: Florentin Dörre <florentin.dorre@neotechnology.com>

Author: adamnsch

README.md

@@ -85,7 +85,7 @@ Full end-to-end examples in Jupyter ready-to-run notebooks can be found in the [
 * [Load data to a projected graph via graph construction](examples/load-data-via-graph-construction.ipynb)
 * [Heterogeneous Node Classification with HashGNN and Autotuning](https://github.com/neo4j/graph-data-science-client/tree/main/examples/heterogeneous-node-classification-with-hashgnn.ipynb)
 * [Perform inference using pre-trained KGE models](examples/kge-predict-transe-pyg-train.ipynb)
-* [Visualize GDS Projections with PyVis](examples/visualize-with-pyvis.ipynb)
+* [Visualize GDS Projections](examples/visualize.ipynb)
 
 
 ## Documentation

doc/modules/ROOT/pages/tutorials/visualize-with-pyvis.adoc renamed to doc/modules/ROOT/pages/tutorials/visualize.adoc

@@ -1,6 +1,6 @@
 // DO NOT EDIT - AsciiDoc file generated automatically
 
-= GDS Projection Visualization with PyVis
+= Visualizing GDS Projections
 
 
 https://colab.research.google.com/github/neo4j/graph-data-science-client/blob/main/examples/import-sample-export-gnn.ipynb[image:https://colab.research.google.com/assets/colab-badge.svg[Open
@@ -92,7 +92,7 @@ sampled.
 
 [source, python, role=no-test]
 ----
-G_sample, _ = gds.alpha.graph.sample.rwr("cora_sample", G, randomSeed=42, concurrency=1)
+G_sample, _ = gds.graph.sample.rwr("cora_sample", G, randomSeed=42, concurrency=1)
 ----
 
 We should have somewhere around 0.15 * 2708 ~ 406 nodes in our sample.
@@ -124,7 +124,7 @@ Let’s start by fetching the relationships.
 
 [source, python, role=no-test]
 ----
-sample_topology_df = gds.beta.graph.relationships.stream(G_sample)
+sample_topology_df = gds.graph.relationships.stream(G_sample)
 display(sample_topology_df)
 ----
 
@@ -174,7 +174,7 @@ net.add_edges(zip(sample_topology_df["sourceNodeId"], sample_topology_df["target
 net.show("cora-sample.html")
 ----
 
-Unsuprisingly we can see that papers largely seem clustered by academic
+Unsurprisingly we can see that papers largely seem clustered by academic
 subject. We also note that some nodes appear larger in size, indicating
 that they have a higher centrality score according to PageRank.
 

doc/modules/ROOT/partials/tutorial-list.adoc

@@ -7,5 +7,6 @@
 * xref:tutorials/node-regression-with-subgraph-and-graph-sample.adoc[]
 * xref:tutorials/heterogeneous-node-classification-with-hashgnn.adoc[]
 * xref:tutorials/kge-predict-transe-pyg-train.adoc[]
+* xref:tutorials/visualize.adoc[]
 * xref:tutorials/gds-sessions.adoc[] (Beta)
 * xref:tutorials/gds-sessions-self-managed.adoc[] (Beta)

examples/visualize-with-pyvis.ipynb renamed to examples/visualize.ipynb

@@ -5,7 +5,7 @@
    "id": "82568b27",
    "metadata": {},
    "source": [
-    "# GDS Projection Visualization with PyVis"
+    "# Visualizing GDS Projections"
    ]
   },
   {
@@ -155,7 +155,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "G_sample, _ = gds.alpha.graph.sample.rwr(\"cora_sample\", G, randomSeed=42, concurrency=1)"
+    "G_sample, _ = gds.graph.sample.rwr(\"cora_sample\", G, randomSeed=42, concurrency=1)"
    ]
   },
   {
@@ -216,7 +216,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample_topology_df = gds.beta.graph.relationships.stream(G_sample)\n",
+    "sample_topology_df = gds.graph.relationships.stream(G_sample)\n",
     "display(sample_topology_df)"
    ]
   },
@@ -290,7 +290,7 @@
    "id": "8f8bc4a2",
    "metadata": {},
    "source": [
-    "Unsuprisingly we can see that papers largely seem clustered by academic subject.\n",
+    "Unsurprisingly we can see that papers largely seem clustered by academic subject.\n",
     "We also note that some nodes appear larger in size, indicating that they have a higher centrality score according to PageRank.\n",
     "\n",
     "We can scroll over the graphic to zoom in/out, and \"click and drag\" the background to navigate to different parts of the network.\n",