better docstrings

graphdatascience/procedure_surface/api/catalog/node_label_endpoints.py

@@ -34,7 +34,7 @@ def mutate(
         node_filter : str
             A Cypher predicate for filtering nodes in the input graph.
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -78,7 +78,7 @@ def write(
         node_filter : str
             A Cypher predicate for filtering nodes in the input graph.
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None

graphdatascience/procedure_surface/api/catalog/node_properties_endpoints.py

@@ -42,7 +42,7 @@ def stream(
         concurrency : Optional[Any], default=None
             The number of concurrent threads
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -89,7 +89,7 @@ def write(
         write_concurrency : Optional[Any], default=None
             The number of concurrent threads used for writing
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -130,7 +130,7 @@ def drop(
         concurrency : Optional[Any], default=None
             The number of concurrent threads
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None

graphdatascience/procedure_surface/api/catalog/relationships_endpoints.py

@@ -39,7 +39,7 @@ def stream(
         concurrency : Optional[Any], default=None
             The number of concurrent threads
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -81,7 +81,7 @@ def write(
         write_concurrency : Optional[Any], default=None
             The number of concurrent threads used for writing
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -146,7 +146,7 @@ def index_inverse(
         concurrency : Optional[Any], default=None
             The number of concurrent threads
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -192,7 +192,7 @@ def to_undirected(
         concurrency : Optional[Any], default=None
             The number of concurrent threads
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None

graphdatascience/procedure_surface/api/catalog_endpoints.py

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

graphdatascience/procedure_surface/api/centrality/articlerank_endpoints.py

@@ -176,7 +176,7 @@ def stream(
         node_labels : Optional[List[str]], default=None
             The node labels used to select nodes for this algorithm run
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None

graphdatascience/procedure_surface/api/centrality/articulationpoints_endpoints.py

@@ -38,7 +38,7 @@ def mutate(
         node_labels : Optional[List[str]], default=None
             The node labels used to select nodes for this algorithm run
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -80,7 +80,7 @@ def stats(
         node_labels : Optional[List[str]], default=None
             The node labels used to select nodes for this algorithm run
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -120,7 +120,7 @@ def stream(
         node_labels : Optional[List[str]], default=None
             The node labels used to select nodes for this algorithm run
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None
@@ -168,7 +168,7 @@ def write(
         node_labels : Optional[List[str]], default=None
             The node labels used to select nodes for this algorithm run
         sudo : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
             Whether to log progress
         username : Optional[str], default=None

graphdatascience/procedure_surface/api/centrality/betweenness_endpoints.py

@@ -7,10 +7,12 @@
 
 from graphdatascience.procedure_surface.api.base_result import BaseResult
 from graphdatascience.procedure_surface.api.catalog.graph_api import GraphV2
+
 from graphdatascience.procedure_surface.api.estimation_result import EstimationResult
 
 
 class BetweennessEndpoints(ABC):
+
     @abstractmethod
     def mutate(
         self,

graphdatascience/procedure_surface/api/centrality/pagerank_endpoints.py

@@ -7,10 +7,12 @@
 
 from graphdatascience.procedure_surface.api.base_result import BaseResult
 from graphdatascience.procedure_surface.api.catalog.graph_api import GraphV2
+
 from graphdatascience.procedure_surface.api.estimation_result import EstimationResult
 
 
 class PageRankEndpoints(ABC):
+
     @abstractmethod
     def mutate(
         self,

graphdatascience/procedure_surface/api/community/k1coloring_endpoints.py

@@ -11,9 +11,6 @@
 
 
 class K1ColoringEndpoints(ABC):
-    """
-    Abstract base class defining the API for the K-1 Coloring algorithm.
-    """
 
     @abstractmethod
     def mutate(
@@ -31,32 +28,35 @@ def mutate(
         job_id: Optional[Any] = 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 : Optional[int], default=None
             The batch size for processing
         max_iterations : Optional[int], default=None
-            The maximum number of iterations of K-1 Coloring to run
+            Maximum number of iterations to run.
         relationship_types : Optional[List[str]], default=None
-            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 : Optional[List[str]], default=None
-            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 : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
-            Whether to log progress
+            Display progress logging.
         username : Optional[str], 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 : Optional[Any], default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         job_id : Optional[Any], default=None
-            An identifier for the job
+            Identifier for the computation.
 
         Returns
         -------

graphdatascience/procedure_surface/api/community/kcore_endpoints.py

@@ -29,28 +29,32 @@ def mutate(
         job_id: Optional[Any] = 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.
         relationship_types : Optional[List[str]], default=None
-            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 : Optional[List[str]], default=None
-            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 : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
-            Whether to log progress
+            Display progress logging.
         username : Optional[str], 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 : Optional[Any], default=None
-            The number of concurrent threads
-        job_id : Optional[Any], default=None
-            An identifier for the job
+            Number of CPU threads to use.
+        job_id : Optional[Any] = None
+            Identifier for the computation.
 
         Returns
         -------

graphdatascience/procedure_surface/api/community/louvain_endpoints.py

@@ -36,42 +36,46 @@ def mutate(
         relationship_weight_property: Optional[str] = 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 : Optional[float], default=None
-            The tolerance value for the algorithm convergence
+            Minimum change in scores between iterations.
         max_levels : Optional[int], default=None
             The maximum number of levels in the hierarchy
         include_intermediate_communities : Optional[bool], default=None
-            Whether to include intermediate community assignments
+            Whether to include intermediate communities
         max_iterations : Optional[int], default=None
-            The maximum number of iterations per level
+            Maximum number of iterations to run.
         relationship_types : Optional[List[str]], default=None
-            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 : Optional[List[str]], default=None
-            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 : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
-            Whether to log progress
+            Display progress logging.
         username : Optional[str], 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 : Optional[Any], default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         job_id : Optional[Any], default=None
-            An identifier for the job
+            Identifier for the computation.
         seed_property : Optional[str], default=None
-            Defines node properties that are used as initial community identifiers
+            The property name that contains seed values
         consecutive_ids : Optional[bool], default=None
-            Flag to decide whether community identifiers are mapped into a consecutive id space
+            Whether to use consecutive IDs
         relationship_weight_property : Optional[str], 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

@@ -30,30 +30,33 @@ def mutate(
         consecutive_ids: Optional[bool] = 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.
         relationship_types : Optional[List[str]], default=None
-            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 : Optional[List[str]], default=None
-            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 : Optional[bool], default=None
-            Override memory estimation limits
+            Disable the memory guard.
         log_progress : Optional[bool], default=None
-            Whether to log progress
+            Display progress logging.
         username : Optional[str], 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 : Optional[Any], default=None
-            The number of concurrent threads
+            Number of CPU threads to use.
         job_id : Optional[Any], default=None
-            An identifier for the job
+            Identifier for the computation.
         consecutive_ids : Optional[bool], default=None
-            Flag to decide whether component identifiers are mapped into a consecutive id space
+            Whether to use consecutive IDs for components
 
         Returns
         -------