Centrality stats/write docstrings

Author: Mats-SX

graphdatascience/procedure_surface/api/centrality/articlerank_endpoints.py

@@ -100,7 +100,11 @@ def stats(
         source_nodes: Optional[Any] = None,
     ) -> ArticleRankStatsResult:
         """
-        Executes the ArticleRank algorithm and returns result statistics without writing the result to Neo4j.
+        Runs the Article Rank algorithm and returns result statistics without storing the results.
+
+        ArticleRank is a variant of the Page Rank algorithm, which measures the transitive influence of nodes.
+        Page Rank follows the assumption that relationships originating from low-degree nodes have a higher influence than relationships from high-degree nodes.
+        Article Rank lowers the influence of low-degree nodes by lowering the scores being sent to their neighbors in each iteration.
 
         Parameters
         ----------
@@ -218,7 +222,11 @@ def write(
         write_concurrency: Optional[int] = None,
     ) -> ArticleRankWriteResult:
         """
-        Executes the ArticleRank algorithm and writes the results to Neo4j.
+        Runs the Article Rank algorithm and stores the result in the Neo4j database as a new node property.
+
+        ArticleRank is a variant of the Page Rank algorithm, which measures the transitive influence of nodes.
+        Page Rank follows the assumption that relationships originating from low-degree nodes have a higher influence than relationships from high-degree nodes.
+        Article Rank lowers the influence of low-degree nodes by lowering the scores being sent to their neighbors in each iteration.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/articulationpoints_endpoints.py

@@ -73,7 +73,10 @@ def stats(
         job_id: Optional[Any] = None,
     ) -> "ArticulationPointsStatsResult":
         """
-        Executes the ArticulationPoints algorithm and returns result statistics without writing the result to Neo4j.
+        Runs the Articulation Points algorithm and returns result statistics without storing the results.
+
+        Given a graph, an articulation point is a node whose removal increases the number of connected components in the graph.
+        The Neo4j GDS Library provides an efficient linear time sequential algorithm to compute all articulation points in a graph.
 
         Parameters
         ----------
@@ -157,7 +160,10 @@ def write(
         write_concurrency: Optional[Any] = None,
     ) -> "ArticulationPointsWriteResult":
         """
-        Executes the ArticulationPoints algorithm and writes the results back to the Neo4j database.
+        Runs the Articulation Points algorithm and stores the result in the Neo4j database as a new node property.
+
+        Given a graph, an articulation point is a node whose removal increases the number of connected components in the graph.
+        The Neo4j GDS Library provides an efficient linear time sequential algorithm to compute all articulation points in a graph.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/betweenness_endpoints.py

@@ -89,7 +89,12 @@ def stats(
         relationship_weight_property: Optional[str] = None,
     ) -> BetweennessStatsResult:
         """
-        Executes the Betweenness Centrality algorithm and returns result statistics without writing the result to Neo4j.
+        Runs the Betweenness Centrality algorithm and returns result statistics without storing the results.
+
+        Betweenness centrality is a way of detecting the amount of influence a node has over the flow of information in a graph.
+        It is often used to find nodes that serve as a bridge from one part of a graph to another.
+        The algorithm calculates shortest paths between all pairs of nodes in a graph.
+        Each node receives a score, based on the number of shortest paths that pass through the node.
 
         Parameters
         ----------
@@ -189,7 +194,12 @@ def write(
         write_concurrency: Optional[Any] = None,
     ) -> BetweennessWriteResult:
         """
-        Executes the Betweenness Centrality algorithm and writes the results to the Neo4j database.
+        Runs the Betweenness Centrality algorithm and stores the result in the Neo4j database as a new node property.
+
+        Betweenness centrality is a way of detecting the amount of influence a node has over the flow of information in a graph.
+        It is often used to find nodes that serve as a bridge from one part of a graph to another.
+        The algorithm calculates shortest paths between all pairs of nodes in a graph.
+        Each node receives a score, based on the number of shortest paths that pass through the node.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/celf_endpoints.py

@@ -95,7 +95,9 @@ def stats(
         job_id: Optional[Any] = None,
     ) -> CelfStatsResult:
         """
-        Executes the CELF algorithm and returns statistics without writing the result to Neo4j.
+        Runs the CELF algorithm and returns result statistics without storing the results.
+
+        The influence maximization problem asks for a set of k nodes that maximize the expected spread of influence in the network.
 
         Parameters
         ----------
@@ -206,7 +208,9 @@ def write(
         write_concurrency: Optional[Any] = None,
     ) -> CelfWriteResult:
         """
-        Executes the CELF algorithm and writes the results to the Neo4j database.
+        Runs the CELF algorithm and stores the result in the Neo4j database as a new node property.
+
+        The influence maximization problem asks for a set of k nodes that maximize the expected spread of influence in the network.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/closeness_endpoints.py

@@ -86,7 +86,11 @@ def stats(
         job_id: Optional[Any] = None,
     ) -> ClosenessStatsResult:
         """
-        Executes the Closeness Centrality algorithm and returns statistics without writing the result to Neo4j.
+        Runs the Closeness Centrality algorithm and returns result statistics without storing the results.
+
+        Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph.
+        The closeness centrality of a node measures its average farness (inverse distance) to all other nodes.
+        Nodes with a high closeness score have the shortest distances to all other nodes.
 
         Parameters
         ----------
@@ -179,7 +183,11 @@ def write(
         write_concurrency: Optional[Any] = None,
     ) -> ClosenessWriteResult:
         """
-        Executes the Closeness Centrality algorithm and writes the results to the Neo4j database.
+        Runs the Closeness Centrality algorithm and stores the result in the Neo4j database as a new node property.
+
+        Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph.
+        The closeness centrality of a node measures its average farness (inverse distance) to all other nodes.
+        Nodes with a high closeness score have the shortest distances to all other nodes.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/closeness_harmonic_endpoints.py

@@ -81,7 +81,12 @@ def stats(
         job_id: Optional[Any] = None,
     ) -> ClosenessHarmonicStatsResult:
         """
-        Executes the Harmonic Closeness Centrality algorithm and returns statistics without writing the result to Neo4j.
+        Runs the Harmonic Centrality algorithm and returns result statistics without storing the results.
+
+        Harmonic centrality was proposed by Marchiori and Latora while trying to come up with a sensible notion of "average shortest path".
+        They suggested a different way of calculating the average distance to that used in the Closeness Centrality algorithm.
+        Rather than summing the distances of a node to all other nodes, the harmonic centrality algorithm sums the inverse of those distances.
+        This enables it deal with infinite values.
 
         Parameters
         ----------
@@ -167,7 +172,12 @@ def write(
         write_concurrency: Optional[Any] = None,
     ) -> ClosenessHarmonicWriteResult:
         """
-        Executes the Harmonic Closeness Centrality algorithm and writes the results to the Neo4j database.
+        Runs the Harmonic Centrality algorithm and stores the result in the Neo4j database as a new node property.
+
+        Harmonic centrality was proposed by Marchiori and Latora while trying to come up with a sensible notion of "average shortest path".
+        They suggested a different way of calculating the average distance to that used in the Closeness Centrality algorithm.
+        Rather than summing the distances of a node to all other nodes, the harmonic centrality algorithm sums the inverse of those distances.
+        This enables it deal with infinite values.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/degree_endpoints.py

@@ -96,7 +96,12 @@ def stats(
         relationship_weight_property: Optional[str] = None,
     ) -> DegreeStatsResult:
         """
-        Executes the Degree Centrality algorithm and returns statistics without writing the result to Neo4j.
+        Runs the Degree Centrality algorithm and returns result statistics without storing the results.
+
+        The Degree Centrality algorithm can be used to find popular nodes within a graph.
+        The degree centrality measures the number of incoming or outgoing (or both) relationships from a node, which can be defined by the orientation of a relationship projection.
+        It can be applied to either weighted or unweighted graphs.
+        In the weighted case the algorithm computes the sum of all positive weights of adjacent relationships of a node, for each node in the graph.
 
         Parameters
         ----------
@@ -205,7 +210,12 @@ def write(
         write_concurrency: Optional[Any] = None,
     ) -> DegreeWriteResult:
         """
-        Executes the Degree Centrality algorithm and writes the results to the Neo4j database.
+        Runs the Degree Centrality algorithm and stores the result in the Neo4j database as a new node property.
+
+        The Degree Centrality algorithm can be used to find popular nodes within a graph.
+        The degree centrality measures the number of incoming or outgoing (or both) relationships from a node, which can be defined by the orientation of a relationship projection.
+        It can be applied to either weighted or unweighted graphs.
+        In the weighted case the algorithm computes the sum of all positive weights of adjacent relationships of a node, for each node in the graph.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/eigenvector_endpoints.py

@@ -104,7 +104,12 @@ def stats(
         job_id: Optional[Any] = None,
     ) -> EigenvectorStatsResult:
         """
-        Executes the Eigenvector Centrality algorithm and returns statistics without writing the result to Neo4j.
+        Runs the Eigenvector Centrality algorithm and returns result statistics without storing the results.
+
+        Eigenvector Centrality is an algorithm that measures the transitive influence of nodes.
+        Relationships originating from high-scoring nodes contribute more to the score of a node than connections from low-scoring nodes.
+        A high eigenvector score means that a node is connected to many nodes who themselves have high scores.
+        The algorithm computes the eigenvector associated with the largest absolute eigenvalue.
 
         Parameters
         ----------
@@ -220,7 +225,12 @@ def write(
         write_concurrency: Optional[Any] = None,
     ) -> EigenvectorWriteResult:
         """
-        Executes the Eigenvector Centrality algorithm and writes the results to the Neo4j database.
+        Runs the Eigenvector Centrality algorithm and stores the result in the Neo4j database as a new node property.
+
+        Eigenvector Centrality is an algorithm that measures the transitive influence of nodes.
+        Relationships originating from high-scoring nodes contribute more to the score of a node than connections from low-scoring nodes.
+        A high eigenvector score means that a node is connected to many nodes who themselves have high scores.
+        The algorithm computes the eigenvector associated with the largest absolute eigenvalue.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/pagerank_endpoints.py

@@ -100,7 +100,10 @@ def stats(
         source_nodes: Optional[Any] = None,
     ) -> PageRankStatsResult:
         """
-        Executes the PageRank algorithm and returns statistics.
+        Runs the PageRank algorithm and returns result statistics without storing the results.
+
+        The PageRank algorithm measures the importance of each node within the graph, based on the number of incoming relationships and the importance of the corresponding source nodes.
+        The underlying assumption roughly speaking is that a page is only as important as the pages that link to it.
 
         Parameters
         ----------
@@ -220,7 +223,10 @@ def write(
         write_concurrency: Optional[int] = None,
     ) -> PageRankWriteResult:
         """
-        Executes the PageRank algorithm and writes the results back to the database.
+        Runs the PageRank algorithm and stores the result in the Neo4j database as a new node property.
+
+        The PageRank algorithm measures the importance of each node within the graph, based on the number of incoming relationships and the importance of the corresponding source nodes.
+        The underlying assumption roughly speaking is that a page is only as important as the pages that link to it.
 
         Parameters
         ----------