better docstrings

graphdatascience/procedure_surface/api/catalog/catalog_endpoints.py

@@ -118,7 +118,7 @@ def generate(
         job_id : str | None, default=None
             Unique identifier for the job associated with the graph generation.
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress during graph generation.
         username : str | None, default=None

graphdatascience/procedure_surface/api/catalog/node_label_endpoints.py

@@ -33,7 +33,7 @@ def mutate(
         node_filter : str
             A Cypher predicate for filtering nodes in the input graph.
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None
@@ -77,7 +77,7 @@ def write(
         node_filter : str
             A Cypher predicate for filtering nodes in the input graph.
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None

graphdatascience/procedure_surface/api/catalog/node_properties_endpoints.py

@@ -43,7 +43,7 @@ def stream(
         concurrency : int | None
             The number of concurrent threads
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None
@@ -90,7 +90,7 @@ def write(
         write_concurrency : int | None
             The number of concurrent threads used for writing
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None

graphdatascience/procedure_surface/api/catalog/relationships_endpoints.py

@@ -40,7 +40,7 @@ def stream(
         concurrency : int | None, default=None
             The number of concurrent threads
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool = True
             Whether to log progress
         username : str | None, default=None
@@ -82,7 +82,7 @@ def write(
         write_concurrency : int | None, default=None
             The number of concurrent threads used for writing
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None
@@ -147,7 +147,7 @@ def index_inverse(
         concurrency : int | None, default=None
             The number of concurrent threads
         sudo : bool = False,
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool = True
             Whether to log progress
         username : str | None, default=None
@@ -193,7 +193,7 @@ def to_undirected(
         concurrency : int | None, default=None
             The number of concurrent threads
         sudo : bool = False,
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool = True
             Whether to log progress
         username : str | None, default=None

graphdatascience/procedure_surface/api/centrality/articlerank_endpoints.py

@@ -195,7 +195,7 @@ def stream(
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool
             Whether to log progress
         username : str | None, default=None

graphdatascience/procedure_surface/api/centrality/articulationpoints_endpoints.py

@@ -41,7 +41,7 @@ def mutate(
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None
@@ -83,7 +83,7 @@ def stats(
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None
@@ -123,7 +123,7 @@ def stream(
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None
@@ -171,7 +171,7 @@ def write(
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
         sudo : bool
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool | None, default=None
             Whether to log progress
         username : str | None, default=None

graphdatascience/procedure_surface/api/centrality/betweenness_endpoints.py

@@ -12,6 +12,7 @@
 
 
 class BetweennessEndpoints(ABC):
+
     @abstractmethod
     def mutate(
         self,

graphdatascience/procedure_surface/api/centrality/pagerank_endpoints.py

@@ -13,6 +13,7 @@
 
 
 class PageRankEndpoints(ABC):
+
     @abstractmethod
     def mutate(
         self,

graphdatascience/procedure_surface/api/community/k1coloring_endpoints.py

@@ -12,9 +12,6 @@
 
 
 class K1ColoringEndpoints(ABC):
-    """
-    Abstract base class defining the API for the K-1 Coloring algorithm.
-    """
 
     @abstractmethod
     def mutate(
@@ -33,32 +30,35 @@ def mutate(
         username: str | None = None,
     ) -> K1ColoringMutateResult:
         """
-        Executes the K-1 Coloring algorithm and writes the results to the in-memory graph as node properties.
+        Runs the K-1 Coloring algorithm and stores the results in the graph catalog as a new node property.
+
+        The K-1 Coloring algorithm assigns a color to every node in the graph, trying to optimize for two objectives:
+        to make sure that every neighbor of a given node has a different color than the node itself, and to use as few colors as possible.
 
         Parameters
         ----------
         G : GraphV2
             The graph to run the algorithm on
         mutate_property : str
-            The property name to store the color for each node
+            Name of the node property to store the results in.
         batch_size : int, default=10000
             The batch size for processing
         concurrency : int | None, default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         job_id : str | None, default=None
-            An identifier for the job
+            Identifier for the computation.
         log_progress : bool, default=True
-            Whether to log progress
+            Display progress logging.
         max_iterations : int, default=10
-            The maximum number of iterations of K-1 Coloring to run
+            Maximum number of iterations to run.
         node_labels : list[str]
-            The node labels used to select nodes for this algorithm run
+            Filter the graph using the given node labels. Nodes with any of the given labels will be included.
         relationship_types : list[str]
-            The relationship types used to select relationships for this algorithm run
+            Filter the graph using the given relationship types. Relationships with any of the given types will be included.
         sudo : bool, default=False
-            Override memory estimation limits
+            Disable the memory guard.
         username : str | None, default=None
-            The username to attribute the procedure run to
+            As an administrator, run the algorithm as a different user, to access also their graphs.
 
         Returns
         -------

graphdatascience/procedure_surface/api/community/kcore_endpoints.py

@@ -31,28 +31,32 @@ def mutate(
         username: str | None = None,
     ) -> KCoreMutateResult:
         """
-        Executes the K-Core algorithm and writes the results to the in-memory graph as node properties.
+        Runs the K-Core Decomposition algorithm and stores the results in the graph catalog as a new node property.
+
+        The K-core decomposition constitutes a process that separates the nodes in a graph into groups based on the degree sequence and topology of the graph.
+        The term `i-core` refers to a maximal subgraph of the original graph such that each node in this subgraph has degree at least `i`.
+        Each node is associated with a core value which denotes the largest value `i` such that the node belongs to the `i-core`.
 
         Parameters
         ----------
         G : GraphV2
             The graph to run the algorithm on
         mutate_property : str
-            The property name to store the core value for each node
+            Name of the node property to store the results in.
         concurrency : int | None, default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         job_id : str | None, default=None
-            An identifier for the job
+            Identifier for the computation.
         log_progress : bool, default=True
-            Whether to log progress
+            Display progress logging.
         node_labels : list[str]
-            The node labels used to select nodes for this algorithm run
+            Filter the graph using the given node labels. Nodes with any of the given labels will be included.
         relationship_types : list[str]
-            The relationship types used to select relationships for this algorithm run
+            Filter the graph using the given relationship types. Relationships with any of the given types will be included.
         sudo : bool, default=False
-            Override memory estimation limits
+            Disable the memory guard.
         username : str | None, default=None
-            The username to attribute the procedure run to
+            As an administrator, run the algorithm as a different user, to access also their graphs.
 
         Returns
         -------

graphdatascience/procedure_surface/api/community/louvain_endpoints.py

@@ -37,42 +37,46 @@ def mutate(
         relationship_weight_property: str | None = None,
     ) -> LouvainMutateResult:
         """
-        Executes the Louvain algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Louvain algorithm and stores the results in the graph catalog as a new node property.
+
+        The Louvain method is an algorithm to detect communities in large networks.
+        It maximizes a modularity score for each community, where the modularity quantifies the quality of an assignment of nodes to communities by evaluating how much more densely connected the nodes within a community are, compared to how connected they would be in a random network.
+        The Louvain algorithm is a hierarchical clustering algorithm that recursively merges communities into a single node and runs the modularity clustering on the condensed graphs.
 
         Parameters
         ----------
         G : GraphV2
             The graph to run the algorithm on
         mutate_property : str
-            The property name to store the community ID for each node
+            Name of the node property to store the results in.
         tolerance : float, default=0.0001
-            The tolerance value for the algorithm convergence
+            Minimum change in scores between iterations.
         max_levels : int, default=10
             The maximum number of levels in the hierarchy
         include_intermediate_communities : bool, default=False
-            Whether to include intermediate community assignments
+            Whether to include intermediate communities
         max_iterations : int, default=10
-            The maximum number of iterations per level
+            Maximum number of iterations to run.
         relationship_types : list[str]
-            The relationships types used to select relationships for this algorithm run
+            Filter the graph using the given relationship types. Relationships with any of the given types will be included.
         node_labels : list[str]
-            The node labels used to select nodes for this algorithm run
+            Filter the graph using the given node labels. Nodes with any of the given labels will be included.
         sudo : bool, default=False
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool, default=True
-            Whether to log progress
+            Display progress logging.
         username : str | None, default=None
-            The username to attribute the procedure run to
+            As an administrator, run the algorithm as a different user, to access also their graphs.
         concurrency : int | None, default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         job_id : str | None, default=None
-            An identifier for the job
+            Identifier for the computation.
         seed_property : str | None, default=None
-            Defines node properties that are used as initial community identifiers
+            The property name that contains seed values
         consecutive_ids : bool, default=False
-            Flag to decide whether community identifiers are mapped into a consecutive id space
+            Whether to use consecutive IDs
         relationship_weight_property : str | None, default=None
-            The property name that contains weight
+            Name of the property to be used as weights.
 
         Returns
         -------

graphdatascience/procedure_surface/api/community/scc_endpoints.py

@@ -32,30 +32,33 @@ def mutate(
         username: str | None = None,
     ) -> SccMutateResult:
         """
-        Executes the SCC algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Strongly Connected Components algorithm and stores the results in the graph catalog as a new node property.
+
+        The Strongly Connected Components (SCC) algorithm finds maximal sets of connected nodes in a directed graph.
+        A set is considered a strongly connected component if there is a directed path between each pair of nodes within the set.
 
         Parameters
         ----------
         G : GraphV2
             The graph to run the algorithm on
         mutate_property : str
-            The property name to store the component ID for each node
+            Name of the node property to store the results in.
         concurrency : int | None, default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         consecutive_ids : bool, default=False
-            Flag to decide whether component identifiers are mapped into a consecutive id space
+            Whether to use consecutive IDs for components
         job_id : str | None, default=None
-            An identifier for the job
+            Identifier for the computation.
         log_progress : bool, default=True
-            Whether to log progress
+            Display progress logging.
         node_labels : list[str]
-            The node labels used to select nodes for this algorithm run
+            Filter the graph using the given node labels. Nodes with any of the given labels will be included.
         relationship_types : list[str]
-            The relationship types used to select relationships for this algorithm run
+            Filter the graph using the given relationship types. Relationships with any of the given types will be included.
         sudo : bool, default=False
-            Override memory estimation limits
+            Disable the memory guard.
         username : str | None, default=None
-            The username to attribute the procedure run to
+            As an administrator, run the algorithm as a different user, to access also their graphs.
 
         Returns
         -------

graphdatascience/procedure_surface/api/community/wcc_endpoints.py

@@ -34,36 +34,39 @@ def mutate(
         relationship_weight_property: str | None = None,
     ) -> WccMutateResult:
         """
-        Executes the WCC algorithm and writes the results to the in-memory graph as node properties.
+        Runs the Weakly Connected Components algorithm and stores the results in the graph catalog as a new node property.
+
+        The Weakly Connected Components (WCC) algorithm finds sets of connected nodes in directed and undirected graphs where two nodes are connected if there exists a path between them.
+        In contrast to Strongly Connected Components (SCC), the direction of relationships on the path between two nodes is not considered.
 
         Parameters
         ----------
         G : GraphV2
             The graph to run the algorithm on
         mutate_property : str
-            The property name to store the component ID for each node
+            Name of the node property to store the results in.
         threshold : float, default=0.0
             The minimum required weight to consider a relationship during traversal
         relationship_types : list[str]
-            The relationships types used to select relationships for this algorithm run
+            Filter the graph using the given relationship types. Relationships with any of the given types will be included.
         node_labels : list[str]
-            The node labels used to select nodes for this algorithm run
+            Filter the graph using the given node labels. Nodes with any of the given labels will be included.
         sudo : bool, default=False
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : bool, default=True
-            Whether to log progress
+            Display progress logging.
         username : str | None, default=None
-            The username to attribute the procedure run to
+            As an administrator, run the algorithm as a different user, to access also their graphs.
         concurrency : int | None, default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         job_id : str | None, default=None
-            An identifier for the job
+            Identifier for the computation.
         seed_property : str | None, default=None
-            Defines node properties that are used as initial component identifiers
+            The property name that contains seed values
         consecutive_ids : bool, default=False
-            Flag to decide whether component identifiers are mapped into a consecutive id space
+            Whether to use consecutive IDs for components
         relationship_weight_property : str | None, default=None
-            The property name that contains weight
+            Name of the property to be used as weights.
 
         Returns
         -------