Update to pandoc 3.6.2

Author: FlorentinD

doc/README.md

@@ -134,7 +134,7 @@ The script `../scripts/nb2doc/convert.sh` can be used:
 * to generate documents from new notebooks;
 * to ensure that any changes to the existing notebooks are reflected in the existing documents (for instance in a CI setting).
 
-The script must be run from the project root directory and requires [Pandoc](https://pandoc.org/) to be already installed. The latest supported version of Pandoc is 3.5
+The script must be run from the project root directory and requires [Pandoc](https://pandoc.org/) to be already installed. The latest supported version of Pandoc is 3.6.2
 
 ```bash
 ./scripts/nb2doc/convert.sh

doc/modules/ROOT/pages/tutorials/centrality-algorithms.adoc

@@ -13,7 +13,7 @@ in the Neo4j Graph Data Science Client Github repository.
 
 Centrality algorithms are used to understand the role or influence of
 particular nodes in a graph. The notebook shows the application of
-centrality algorithms using the `+graphdatascience+` library on the
+centrality algorithms using the `graphdatascience` library on the
 Airline travel reachability network dataset that can be downloaded
 https://snap.stanford.edu/data/reachability.html[here].
 
@@ -64,10 +64,10 @@ assert gds.server_version() >= ServerVersion(1, 8, 0)
 == Importing the dataset
 
 We import the dataset as a pandas dataframe first. We deal with two
-files here. The file `+reachability-meta.csv.gz+` stores the names of
-the cities and their information while the file `+reachability.txt.gz+`
-stores the edges of the graph. An edge exists from city `+i+` to city
-`+j+` if the estimated airline travel time is less than a threshold.
+files here. The file `reachability-meta.csv.gz` stores the names of the
+cities and their information while the file `reachability.txt.gz` stores
+the edges of the graph. An edge exists from city `i` to city `j` if the
+estimated airline travel time is less than a threshold.
 
 [source, python, role=no-test]
 ----
@@ -88,11 +88,11 @@ routes_df = pd.read_csv(
 routes_df.head()
 ----
 
-Since this graph is very small, a straight-forward Cypher `+UNWIND+`
-query is the simplest way to create our graph in the database.
+Since this graph is very small, a straight-forward Cypher `UNWIND` query
+is the simplest way to create our graph in the database.
 
 Larger graphs may need a more sophisticated importing technique like
-batching, `+neo4j-admin import+` or Arrow `+CREATE DATABASE+`.
+batching, `neo4j-admin import` or Arrow `CREATE DATABASE`.
 
 [source, python, role=no-test]
 ----

doc/modules/ROOT/pages/tutorials/community-detection.adoc

@@ -11,11 +11,11 @@ This Jupyter notebook is hosted
 https://github.com/neo4j/graph-data-science-client/blob/main/examples/community-detection.ipynb[here]
 in the Neo4j Graph Data Science Client Github repository.
 
-The notebook shows the usage of the `+graphdatascience+` library for
+The notebook shows the usage of the `graphdatascience` library for
 community detection on the Reddit Hyperlink Network dataset that can be
 downloaded
 https://snap.stanford.edu/data/soc-RedditHyperlinks.html[here]. We will
-use the `+soc-redditHyperlinks-body.tsv+` file.
+use the `soc-redditHyperlinks-body.tsv` file.
 
 The tasks we cover here include performing initial graph preprocessing
 using Weakly Connected Components and then performing community
@@ -73,8 +73,8 @@ df = df[df["TIMESTAMP"] < "2014-03-01 02:51:13"]
 df.head()
 ----
 
-The `+LINK_SENTIMENT+` column tells if there is a positive (+1) or
-negative (-1) relationship from the source subreddit to destination
+The `LINK++_++SENTIMENT` column tells if there is a positive ({plus}1)
+or negative (-1) relationship from the source subreddit to destination
 subreddit. We filter out the negative sentiment relationships as they
 won’t add to any meaningful communities. We also drop duplicate
 relationships.
@@ -172,7 +172,7 @@ G.node_properties()
 Next, we will see the size of each connected component and depending on
 that, we can pick the subgraph that needs further analysis.
 
-We use `+run_cypher+` here instead of the direct GDS client call since
+We use `run++_++cypher` here instead of the direct GDS client call since
 we want to see the size of the connected components.
 
 [source, python, role=no-test]
@@ -213,7 +213,7 @@ largest_component_graph
 We use the
 https://neo4j.com/docs/graph-data-science/current/algorithms/louvain/[Louvain]
 algorithm to detect communities in our subgraph and assign a
-`+louvainCommunityId+` to each community.
+`louvainCommunityId` to each community.
 
 [source, python, role=no-test]
 ----

doc/modules/ROOT/pages/tutorials/fastrp-and-knn.adoc

@@ -11,7 +11,7 @@ This Jupyter notebook is hosted
 https://github.com/neo4j/graph-data-science-client/blob/main/examples/fastrp-and-knn.ipynb[here]
 in the Neo4j Graph Data Science Client Github repository.
 
-The notebook exemplifies how to use the `+graphdatascience+` Python
+The notebook exemplifies how to use the `graphdatascience` Python
 library to operate Neo4j GDS. It shows an adapted version of the FastRP
 and kNN end-to-end example from the GDS Manual, found
 https://neo4j.com/docs/graph-data-science/current/end-to-end-examples/fastrp-knn-example[here].
@@ -33,11 +33,11 @@ one of the customers will be recommended to the other.
 == Prerequisites
 
 Running this notebook requires a Neo4j server with a recent version
-(2.0+) of GDS installed. We recommend using Neo4j Desktop with GDS, or
-AuraDS.
+(2.0{plus}) of GDS installed. We recommend using Neo4j Desktop with GDS,
+or AuraDS.
 
-The `+graphdatascience+` Python library needs to be installed as well.
-See the examples in the Setup section below and in the
+The `graphdatascience` Python library needs to be installed as well. See
+the examples in the Setup section below and in the
 https://neo4j.com/docs/graph-data-science-client/current/installation/[client
 installation instructions].
 
@@ -80,7 +80,7 @@ assert gds.server_version() >= ServerVersion(1, 8, 0)
 == Example graph creation
 
 We now create a graph of products and customers in the database. The
-`+amount+` relationship property represents the average weekly amount of
+`amount` relationship property represents the average weekly amount of
 money spent by a customer on a given product.
 
 [source, python, role=no-test]
@@ -165,10 +165,10 @@ print(f"Graph '{G.name()}' node labels: {G.node_labels()}")
 Next we use the
 https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/fastrp/[FastRP
 algorithm] to generate node embeddings that capture topological
-information from the graph. We choose to work with
-`+embeddingDimension+` set to 4 which is sufficient since our example
-graph is very small. The `+iterationWeights+` are chosen empirically to
-yield sensible results. Please see
+information from the graph. We choose to work with `embeddingDimension`
+set to 4 which is sufficient since our example graph is very small. The
+`iterationWeights` are chosen empirically to yield sensible results.
+Please see
 https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/fastrp/#algorithms-embeddings-fastrp-syntax[the
 syntax section of the FastRP documentation] for more information on
 these parameters.
@@ -212,11 +212,11 @@ print(f"Number of embedding vectors produced: {result['nodePropertiesWritten']}"
 Now we can run
 https://neo4j.com/docs/graph-data-science/current/algorithms/knn/[kNN]
 to identify similar nodes by using the node embeddings that we generated
-with FastRP as `+nodeProperties+`. Since we are working with a small
-graph, we can set `+sampleRate+` to 1 and `+deltaThreshold+` to 0
-without having to worry about long computation times. The
-`+concurrency+` parameter is set to 1 (along with the fixed
-`+randomSeed+`) in order to get a deterministic result. Please see
+with FastRP as `nodeProperties`. Since we are working with a small
+graph, we can set `sampleRate` to 1 and `deltaThreshold` to 0 without
+having to worry about long computation times. The `concurrency`
+parameter is set to 1 (along with the fixed `randomSeed`) in order to
+get a deterministic result. Please see
 https://neo4j.com/docs/graph-data-science/current/algorithms/knn/#algorithms-knn-syntax[the
 syntax section of the kNN documentation] for more information on these
 parameters.
@@ -252,10 +252,10 @@ paths between nodes leading to many similar FastRP node embeddings.
 == Exploring the results
 
 Let us now inspect the results of our kNN call by using Cypher. We can
-use the `+SIMILARITY+` relationship type to filter out the relationships
+use the `SIMILARITY` relationship type to filter out the relationships
 we are interested in. And since we just care about similarities between
 people for our product recommendation engine, we make sure to only match
-nodes with the `+Person+` label.
+nodes with the `Person` label.
 
 Please see https://neo4j.com/docs/cypher-manual/current/[the Cypher
 manual] for documentation on how to use Cypher.
@@ -271,23 +271,23 @@ gds.run_cypher(
 )
 ----
 
-Our kNN results indicate among other things that the `+Person+` nodes
-named "`Annie`" and "`Matt`" are very similar. Looking at the `+BUYS+`
+Our kNN results indicate among other things that the `Person` nodes
+named "`Annie`" and "`Matt`" are very similar. Looking at the `BUYS`
 relationships for these two nodes we can see that such a conclusion
 makes sense. They both buy three products, two of which are the same
-(`+Product+` nodes named "`Cucumber`" and "`Tomatoes`") for both people
+(`Product` nodes named "`Cucumber`" and "`Tomatoes`") for both people
 and with similar amounts. We can therefore have high confidence in our
 approach.
 
 == Making recommendations
 
-Using the information we derived that the `+Person+` nodes named
-"`Annie`" and "`Matt`" are similar, we can make product recommendations
-for each of them. Since they are similar, we can assume that products
-purchased by only one of the people may be of interest to buy also for
-the other person not already buying the product. By this principle we
-can derive product recommendations for the `+Person+` named "`Matt`"
-using a simple Cypher query.
+Using the information we derived that the `Person` nodes named "`Annie`"
+and "`Matt`" are similar, we can make product recommendations for each
+of them. Since they are similar, we can assume that products purchased
+by only one of the people may be of interest to buy also for the other
+person not already buying the product. By this principle we can derive
+product recommendations for the `Person` named "`Matt`" using a simple
+Cypher query.
 
 [source, python, role=no-test]
 ----
@@ -326,5 +326,5 @@ derive some sensible product recommendations for a customer in our small
 example.
 
 To make sure to get similarities to other customers for every customer
-in our graph with kNN, we could play around with increasing the `+topK+`
+in our graph with kNN, we could play around with increasing the `topK`
 parameter.

doc/modules/ROOT/pages/tutorials/gds-sessions-self-managed.adoc

@@ -11,7 +11,7 @@ This Jupyter notebook is hosted
 https://github.com/neo4j/graph-data-science-client/blob/main/examples/gds-sessions-self-managed.ipynb[here]
 in the Neo4j Graph Data Science Client Github repository.
 
-The notebook shows how to use the `+graphdatascience+` Python library to
+The notebook shows how to use the `graphdatascience` Python library to
 create, manage, and use a GDS Session.
 
 We consider a graph of people and fruits, which we’re using as a simple
@@ -28,13 +28,11 @@ This notebook requires having a Neo4j instance instance available and
 that the GDS sessions feature is enabled for your Neo4j Aura tenant.
 Contact your account manager to get the features enabled.
 
-We also need to have the `+graphdatascience+` Python library installed,
-version `+1.12a1+` or later.
+We also need to have the `graphdatascience` Python library installed,
+version `1.12a1` or later.
 
 [source, python, role=no-test]
 ----
-from datetime import timedelta
-
 %pip install "graphdatascience>=1.13"
 ----
 
@@ -45,7 +43,7 @@ we need to have
 https://neo4j.com/docs/aura/platform/api/authentication/#_creating_credentials[Aura
 API credentialsn].
 
-Using these credentials, we can create our `+GdsSessions+` object, which
+Using these credentials, we can create our `GdsSessions` object, which
 is the main entry point for managing GDS Sessions.
 
 [source, python, role=no-test]
@@ -65,16 +63,16 @@ sessions = GdsSessions(api_credentials=AuraAPICredentials(client_id, client_secr
 
 == Creating a new session
 
-A new session is created by calling `+sessions.get_or_create()+`. As the
-data source, we assume that a self-managed Neo4j DBMS instance has been
-set up and is accessible. We need to pass the database address, user
-name and password to the `+DbmsConnectionInfo+` class.
+A new session is created by calling `sessions.get++_++or++_++create()`.
+As the data source, we assume that a self-managed Neo4j DBMS instance
+has been set up and is accessible. We need to pass the database address,
+user name and password to the `DbmsConnectionInfo` class.
 
 We also need to specify the session size. Please refer to the API
 reference documentation or the manual for a full list.
 
 Finally, we need to give our session a name. We will call ours
-`+people-and-fruits-sm'. It is possible to reconnect to an existing session by calling+`get_or_create`
+`people-and-fruits-sm'. It is possible to reconnect to an existing session by calling`get++_++or++_++create++`++
 with the same session name and configuration.
 
 We will also set a time-to-live (TTL) for the session. This ensures that
@@ -102,6 +100,7 @@ cloud_location = cloud_locations[0]
 [source, python, role=no-test]
 ----
 import os
+from datetime import timedelta
 
 from graphdatascience.session import DbmsConnectionInfo
 
@@ -128,7 +127,12 @@ what that looks like
 
 [source, python, role=no-test]
 ----
+from pandas import DataFrame
+
 gds_sessions = sessions.list()
+
+# for better visualization
+DataFrame(gds_sessions)
 ----
 
 == Adding a dataset
@@ -188,14 +192,14 @@ gds.run_cypher("MATCH (n) RETURN count(n) AS nodeCount")
 == Projecting Graphs
 
 Now that we have imported a graph to our database, we can project it
-into our GDS Session. We do that by using the `+gds.graph.project()+`
+into our GDS Session. We do that by using the `gds.graph.project()`
 endpoint.
 
-The remote projection query that we are using selects all `+Person+`
-nodes and their `+LIKES+` relationships, and all `+Fruit+` nodes and
-their `+LIKES+` relationships. Additionally, we project node properties
-for illustrative purposes. We can use these node properties as input to
-algorithms, although we do not do that in this notebook.
+The remote projection query that we are using selects all `Person` nodes
+and their `LIKES` relationships, and all `Fruit` nodes and their `LIKES`
+relationships. Additionally, we project node properties for illustrative
+purposes. We can use these node properties as input to algorithms,
+although we do not do that in this notebook.
 
 [source, python, role=no-test]
 ----
@@ -275,16 +279,16 @@ the PageRank and FastRP algorithms to the Neo4j database.
 gds.graph.nodeProperties.write(G, ["pagerank", "fastRP"])
 ----
 
-Of course, we can just use `+.write+` modes as well. Let’s run Louvain
-in write mode to show:
+Of course, we can just use `.write` modes as well. Let’s run Louvain in
+write mode to show:
 
 [source, python, role=no-test]
 ----
 gds.louvain.write(G, writeProperty="louvain")
 ----
 
-We can now use the `+gds.run_cypher()+` method to query the updated
-graph. Note that the `+run_cypher()+` method will run the query on the
+We can now use the `gds.run++_++cypher()` method to query the updated
+graph. Note that the `run++_++cypher()` method will run the query on the
 Neo4j database.
 
 [source, python, role=no-test]