neo4j · FlorentinD · Oct 15, 2024
diff --git a/doc/modules/ROOT/pages/tutorials/centrality-algorithms.adoc b/doc/modules/ROOT/pages/tutorials/centrality-algorithms.adoc
@@ -101,8 +101,8 @@ gds.run_cypher(
 
 gds.run_cypher(
     """
-    UNWIND $rels AS rel 
-    MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination}) 
+    UNWIND $rels AS rel
+    MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination})
     CREATE (source)-[:HAS_FLIGHT_TO]->(target)
     """,
     params={"rels": routes_df.to_dict("records")},

diff --git a/doc/modules/ROOT/pages/tutorials/community-detection.adoc b/doc/modules/ROOT/pages/tutorials/community-detection.adoc
@@ -114,8 +114,8 @@ gds.run_cypher(
 
 gds.run_cypher(
     """
-    UNWIND $rels AS rel 
-    MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT}) 
+    UNWIND $rels AS rel
+    MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT})
     CREATE (source)-[:HYPERLINKED_TO]->(target)
     """,
     params={"rels": relationship_df.to_dict("records")},
@@ -232,7 +232,7 @@ We can also check that the property was written by the below command.
 ----
 gds.run_cypher(
     """
-    MATCH (n) WHERE 'louvainCommunityId' IN keys(n) 
+    MATCH (n) WHERE 'louvainCommunityId' IN keys(n)
     RETURN n.name, n.louvainCommunityId LIMIT 10
     """
 )

diff --git a/...odules/ROOT/pages/tutorials/heterogeneous-node-classification-with-hashgnn.adoc b/...odules/ROOT/pages/tutorials/heterogeneous-node-classification-with-hashgnn.adoc
@@ -346,7 +346,7 @@ part with something like a
 https://neo4j.com/docs/graph-data-science-client/current/graph-object/#_projecting_a_graph_object[projection
 from a Neo4j database] to create a more realistic production workflow.
 
-== Comparison with other methods
+=== Comparison with other methods
 
 As mentioned we tried to mimic the setup of the benchmarks in the
 NeurIPS paper
@@ -381,7 +381,7 @@ descent compared to the deep learning models above - HashGNN is an
 unsupervised algorithm - HashGNN runs a lot faster (even without a GPU)
 and requires a lot less memory
 
-== Further learning
+=== Further learning
 
 To learn more about the topics covered in this notebook, please check
 out the following pages of the GDS manual:

diff --git a/examples/centrality-algorithms.ipynb b/examples/centrality-algorithms.ipynb
@@ -30,7 +30,7 @@
     "\n",
     "This notebook will show how you can apply eigenvector centrality, betweenness centrality, degree centrality and closeness centrality on a graph dataset.\n",
     "\n",
-    "### Setup\n",
+    "## Setup\n",
     "\n",
     "We start by importing our dependencies and setting up our GDS client connection to the database."
    ]
@@ -92,7 +92,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Importing the dataset\n",
+    "## Importing the dataset\n",
     "\n",
     "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.\n"
    ]
@@ -146,8 +146,8 @@
     "\n",
     "gds.run_cypher(\n",
     "    \"\"\"\n",
-    "    UNWIND $rels AS rel \n",
-    "    MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination}) \n",
+    "    UNWIND $rels AS rel\n",
+    "    MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination})\n",
     "    CREATE (source)-[:HAS_FLIGHT_TO]->(target)\n",
     "    \"\"\",\n",
     "    params={\"rels\": routes_df.to_dict(\"records\")},\n",
@@ -174,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Eigenvector Centrality\n",
+    "## Eigenvector Centrality\n",
     "\n",
     "[Eigenvector centrality](https://neo4j.com/docs/graph-data-science/current/algorithms/eigenvector-centrality/) measures the importance or influence of a node based on its connections to other nodes in the network. A higher eigenvector centrality score suggests that a node is more central and influential within the network.\n",
     "\n",
@@ -289,7 +289,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Betweenness Centrality\n",
+    "## Betweenness Centrality\n",
     "\n",
     "[Betweenness Centrality](https://neo4j.com/docs/graph-data-science/current/algorithms/betweenness-centrality/) quantifies the importance of a node as a bridge or intermediary in the network. It measures how often a node lies on the shortest path between other pairs of nodes. \n",
     "\n",
@@ -367,7 +367,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Degree Centrality\n",
+    "## Degree Centrality\n",
     "\n",
     "[Degree Centrality](https://neo4j.com/docs/graph-data-science/current/algorithms/degree-centrality/) measures the number of connections (edges) a node has in the network. \n",
     "\n",
@@ -445,7 +445,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Cleanup\n",
+    "## Cleanup\n",
     "\n",
     "Before finishing we can clean up the example data from both the GDS in-memory state and the database."
    ]
@@ -474,7 +474,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### References\n",
+    "## References\n",
     "- For the network:\n",
     "Brendan J. Frey and Delbert Dueck. \"Clustering by passing messages between data points.\" Science 315.5814 (2007): 972-976.\n",
     "\n",

diff --git a/examples/community-detection.ipynb b/examples/community-detection.ipynb
@@ -33,7 +33,7 @@
     "\n",
     "The tasks we cover here include performing initial graph preprocessing using Weakly Connected Components and then performing community detection on the largest component using the Louvain algorithm.\n",
     "\n",
-    "### Setup\n",
+    "## Setup\n",
     "\n",
     "We start by importing our dependencies and setting up our GDS client connection to the database."
    ]
@@ -100,7 +100,7 @@
    "id": "48bd8af1",
    "metadata": {},
    "source": [
-    "### Importing the dataset\n",
+    "## Importing the dataset\n",
     "\n",
     "We import the dataset as a pandas dataframe first. We work with only a subset of the dataset. The sampled data is only till 1st March 2014. "
    ]
@@ -187,8 +187,8 @@
     "\n",
     "gds.run_cypher(\n",
     "    \"\"\"\n",
-    "    UNWIND $rels AS rel \n",
-    "    MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT}) \n",
+    "    UNWIND $rels AS rel\n",
+    "    MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT})\n",
     "    CREATE (source)-[:HYPERLINKED_TO]->(target)\n",
     "    \"\"\",\n",
     "    params={\"rels\": relationship_df.to_dict(\"records\")},\n",
@@ -226,7 +226,7 @@
    "id": "9c259471",
    "metadata": {},
    "source": [
-    "### Weakly Connected Components\n",
+    "## Weakly Connected Components\n",
     "\n",
     "A graph dataset need not always be connected. That is, there may not exist a path from every node to \n",
     "every other node in the graph dataset (subgraphs in it may not be connected to each other at all). Hence, we \n",
@@ -332,7 +332,7 @@
    "id": "17942d04",
    "metadata": {},
    "source": [
-    "### Community Detection using Louvain\n",
+    "## Community Detection using Louvain\n",
     "\n",
     "We use the [Louvain](https://neo4j.com/docs/graph-data-science/current/algorithms/louvain/) algorithm to detect communities in our subgraph and assign a `louvainCommunityId` to each community."
    ]
@@ -382,7 +382,7 @@
    "source": [
     "gds.run_cypher(\n",
     "    \"\"\"\n",
-    "    MATCH (n) WHERE 'louvainCommunityId' IN keys(n) \n",
+    "    MATCH (n) WHERE 'louvainCommunityId' IN keys(n)\n",
     "    RETURN n.name, n.louvainCommunityId LIMIT 10\n",
     "    \"\"\"\n",
     ")"
@@ -424,7 +424,7 @@
    "id": "5ed56f82",
    "metadata": {},
    "source": [
-    "### Further ideas\n",
+    "## Further ideas\n",
     "\n",
     "* Inspect the produced communities using [Bloom](https://neo4j.com/docs/bloom-user-guide/current/). You can use rule-based styling based on the community property.\n",
     "* Try to tune more parameters of Louvain and see how the communities differ.\n",
@@ -437,7 +437,7 @@
    "id": "6e00ed7b",
    "metadata": {},
    "source": [
-    "### Cleanup\n",
+    "## Cleanup\n",
     "\n",
     "Before finishing we can clean up the example data from both the GDS in-memory state and the database."
    ]
@@ -471,7 +471,7 @@
    "id": "65dcb952",
    "metadata": {},
    "source": [
-    "### References\n",
+    "## References\n",
     "\n",
     "Srijan Kumar, William L. Hamilton, Jure Leskovec, and Dan Jurafsky. 2018. Community Interaction and Conflict on the Web. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 933–943. https://doi.org/10.1145/3178876.3186141"
    ]

diff --git a/examples/heterogeneous-node-classification-with-hashgnn.ipynb b/examples/heterogeneous-node-classification-with-hashgnn.ipynb
@@ -250,7 +250,7 @@
    "id": "e9d0dbcc",
    "metadata": {},
    "source": [
-    "### The HashGNN node embedding algorithm\n",
+    "## The HashGNN node embedding algorithm\n",
     "\n",
     "As the last part of the training pipeline, there will be an ML training algorithm.\n",
     "If we use the `plot_keywords` directly as our feature input to the ML algorithm, we will not utilize any of the relationship data we have in our graph.\n",

diff --git a/requirements/dev/dev.txt b/requirements/dev/dev.txt
@@ -1,6 +1,6 @@
 ruff == 0.6.1
 mypy == 1.8.0
-nbconvert == 7.11.0
+nbconvert == 7.16.4
 pandas-stubs == 2.2.2.240603
 tox == 4.11.3
 types-setuptools == 68.1.0.1