Address review comments

* wording of sampling intro
* link to python client in graph create index

Co-authored-by: Adam Schill Collberg <[email protected]>
Co-authored-by: Nicola Vitucci <[email protected]>

doc/modules/ROOT/pages/management-ops/graph-creation/index.adoc

@@ -6,8 +6,6 @@
 You can create graphs from different data sources.
 
 * From a Neo4j database by using either of
-// TODO explain why to use which
-
 ** xref:management-ops/graph-creation/graph-project.adoc[Native projection]
 ** xref:management-ops/graph-creation/graph-project-cypher-projection.adoc[Cypher projection] for more complex projections
 * From external sources via an xref:graph-catalog-apache-arrow-ops.adoc[Apache Arrow connection].
@@ -16,6 +14,8 @@ You can create graphs from different data sources.
 ** xref:management-ops/graph-creation/sampling/rwr.adoc[Sampling]
 * From xref:management-ops/graph-creation/graph-generation.adoc[Random data]
 
+Furthermore, the Python client provides several convenient methods to create graphs, for example from link:https://neo4j.com/docs/graph-data-science-client/current/graph-object/#construct[Pandas DataFrames] or some link:https://neo4j.com/docs/graph-data-science-client/current/common-datasets/[well-known datasets].
+
 
 == Graph data model
 

doc/modules/ROOT/pages/management-ops/graph-creation/sampling/cnarw.adoc

@@ -15,7 +15,9 @@ include::partial$/algorithms/shared/algorithm-traits.adoc[]
 
 == Introduction
 
-Graph sampling algorithms are used to reduce the complexity of large and complex graphs while preserving their essential properties. They can help to speed up computation, reduce bias, and ensure privacy, making graph analysis more manageable and accurate. They are widely used in network analysis, machine learning, and social network analysis, among other applications.
+Graph sampling algorithms can be used to reduce the size of large and complex graphs while preserving structural properties.
+This can help to reduce bias, and ensure privacy, and making graph analysis more scalable.
+Sampling algorithms are widely used in machine learning, social network analysis, and many other applications.
 
 The Common Neighbour Aware Random Walk (CNARW) is a graph sampling technique that involves optimizing the selection of the next-hop node.
 It takes into account the number of common neighbours between the current node and the next-hop candidates.

doc/modules/ROOT/pages/management-ops/graph-creation/sampling/index.adoc

@@ -1,7 +1,9 @@
 = Sampling
 :description: This section details creating a new graph by sampling an existing graph within the Neo4j Graph Data Science library.
 
-Graph sampling algorithms are used to reduce the complexity of large and complex graphs while preserving their essential properties. They can help to speed up computation, reduce bias, and ensure privacy, making graph analysis more manageable and accurate. They are widely used in network analysis, machine learning, and social network analysis, among other applications.
+Graph sampling algorithms can be used to reduce the size of large and complex graphs while preserving structural properties.
+This can help to reduce bias, and ensure privacy, and making graph analysis more scalable.
+Sampling algorithms are widely used in machine learning, social network analysis, and many other applications.
 
 
 You can choose between different sampling methods.