Centrality mutate docstrings

Author: Mats-SX

graphdatascience/procedure_surface/api/centrality/articlerank_endpoints.py

@@ -36,7 +36,11 @@ def mutate(
         source_nodes: Optional[Any] = None,
     ) -> ArticleRankMutateResult:
         """
-        Executes the ArticleRank algorithm and writes the results back to the graph as a node property.
+        Runs the Article Rank algorithm and stores the results in the graph catalog 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

@@ -28,7 +28,10 @@ def mutate(
         job_id: Optional[Any] = None,
     ) -> "ArticulationPointsMutateResult":
         """
-        Executes the ArticulationPoints algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Articulation Points algorithm and stores the results in the graph catalog 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

@@ -33,7 +33,12 @@ def mutate(
         relationship_weight_property: Optional[str] = None,
     ) -> BetweennessMutateResult:
         """
-        Executes the Betweenness Centrality algorithm and returns result statistics without persisting the results
+        Runs the Betweenness Centrality algorithm and stores the results in the graph catalog 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

@@ -37,7 +37,9 @@ def mutate(
         job_id: Optional[Any] = None,
     ) -> CelfMutateResult:
         """
-        Executes the CELF algorithm and writes the results to the in-memory graph as node properties.
+        Runs the CELF algorithm and stores the results in the graph catalog 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

@@ -35,7 +35,11 @@ def mutate(
         job_id: Optional[Any] = None,
     ) -> ClosenessMutateResult:
         """
-        Executes the Closeness Centrality algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Closeness Centrality algorithm and stores the results in the graph catalog 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

@@ -34,7 +34,10 @@ def mutate(
         job_id: Optional[Any] = None,
     ) -> ClosenessHarmonicMutateResult:
         """
-        Executes the Harmonic Closeness Centrality algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Harmonic Centrality algorithm and stores the results in the graph catalog as a new node property.
+
+        Harmonic centrality (also known as valued centrality) is a variant of closeness centrality, that was invented to
+        solve the problem the original formula had when dealing with unconnected graphs.
 
         Parameters
         ----------

graphdatascience/procedure_surface/api/centrality/degree_endpoints.py

@@ -36,7 +36,12 @@ def mutate(
         relationship_weight_property: Optional[str] = None,
     ) -> DegreeMutateResult:
         """
-        Executes the Degree Centrality algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Degree Centrality algorithm and stores the results in the graph catalog 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

@@ -40,7 +40,12 @@ def mutate(
         job_id: Optional[Any] = None,
     ) -> EigenvectorMutateResult:
         """
-        Executes the Eigenvector Centrality algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Eigenvector Centrality algorithm and stores the results in the graph catalog 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

@@ -36,7 +36,10 @@ def mutate(
         source_nodes: Optional[Any] = None,
     ) -> PageRankMutateResult:
         """
-        Executes the PageRank algorithm and writes the results to the in-memory graph as node properties.
+        Runs the PageRank algorithm and stores the results in the graph catalog 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
         ----------