Clarify source_nodes type

Author: FlorentinD

graphdatascience/procedure_surface/api/centrality/articlerank_endpoints.py

@@ -31,7 +31,7 @@ def mutate(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> ArticleRankMutateResult:
         """
         Runs the Article Rank algorithm and stores the results in the graph catalog as a new node property.
@@ -74,8 +74,11 @@ def mutate(
             Identifier for the job.
         relationship_weight_property : str | None, default=None
             Name of the property to be used as weights.
-        source_nodes : Any | None, default=None
-            List of node ids to use as starting points. Use a list of list pairs to associate each node with a bias > 0.
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
 
         Returns
         -------
@@ -100,7 +103,7 @@ def stats(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> ArticleRankStatsResult:
         """
         Runs the Article Rank algorithm and returns result statistics without storing the results.
@@ -141,8 +144,11 @@ def stats(
             Identifier for the job.
         relationship_weight_property : str | None, default=None
             Name of the property to be used as weights.
-        source_nodes : Any | None, default=None
-            List of node ids to use as starting points. Use a list of list pairs to associate each node with a bias > 0.
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
 
         Returns
         -------
@@ -167,7 +173,7 @@ def stream(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> DataFrame:
         """
         Executes the ArticleRank algorithm and returns the results as a stream.
@@ -200,8 +206,11 @@ def stream(
             An identifier for the job
         relationship_weight_property : str | None, default=None
             The property name that contains weight
-        source_nodes : Any | None, default=None
-            The source nodes for personalized ArticleRank
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
 
         Returns
         -------
@@ -227,7 +236,7 @@ def write(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
         write_concurrency: int | None = None,
     ) -> ArticleRankWriteResult:
         """
@@ -271,8 +280,11 @@ def write(
             Identifier for the job.
         relationship_weight_property : str | None, default=None
             Name of the property to be used as weights.
-        source_nodes : Any | None, default=None
-            List of node ids to use as starting points. Use a list of list pairs to associate each node with a bias > 0.
+                source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
         write_concurrency : int | None, default=None
             The number of concurrent threads used for writing
 
@@ -295,7 +307,7 @@ def estimate(
         node_labels: list[str] = ALL_LABELS,
         concurrency: int | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> EstimationResult:
         """
         Estimate the memory consumption of an algorithm run.
@@ -320,8 +332,11 @@ def estimate(
             The number of concurrent threads
         relationship_weight_property : str | None, default=None
             The property name that contains weight
-        source_nodes : Any | None, default=None
-            The source nodes for personalized ArticleRank
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
 
         Returns
         -------

graphdatascience/procedure_surface/api/centrality/degree_endpoints.py

@@ -41,7 +41,7 @@ def mutate(
             The graph to run the algorithm on
         mutate_property : str
             Name of the node property to store the results in.
-        orientation : Any | None, default=None
+        orientation : str | None
             The orientation of relationships to consider. Can be 'NATURAL', 'REVERSE', or 'UNDIRECTED'.
         relationship_types : list[str]
             Filter the graph using the given relationship types. Relationships with any of the given types will be included.
@@ -93,7 +93,7 @@ def stats(
         ----------
         G : GraphV2
             The graph to run the algorithm on
-        orientation : Any | None, default=None
+        orientation : str | None
             The orientation of relationships to consider. Can be 'NATURAL', 'REVERSE', or 'UNDIRECTED'.
         relationship_types : list[str]
             Filter the graph using the given relationship types. Relationships with any of the given types will be included.
@@ -140,7 +140,7 @@ def stream(
         ----------
         G : GraphV2
             The graph to run the algorithm on
-        orientation : Any | None, default=None
+        orientation : str | None
             The orientation of relationships to consider. Can be 'NATURAL', 'REVERSE', or 'UNDIRECTED'.
             'NATURAL' (default) respects the direction of relationships as they are stored in the graph.
             'REVERSE' treats each relationship as if it were directed in the opposite direction.
@@ -203,7 +203,7 @@ def write(
             The graph to run the algorithm on
         write_property : str
             The property name to store the degree centrality score for each node in the database
-        orientation : Any | None, default=None
+        orientation : str | None
             The orientation of relationships to consider. Can be 'NATURAL', 'REVERSE', or 'UNDIRECTED'.
         relationship_types : list[str]
             Filter the graph using the given relationship types. Relationships with any of the given types will be included.
@@ -248,7 +248,7 @@ def estimate(
         ----------
         G : GraphV2 | dict[str, Any]
             The graph to run the algorithm on or a dictionary representing the graph.
-        orientation : Any | None, default=None
+        orientation : str | None
             The orientation of relationships to consider. Can be 'NATURAL', 'REVERSE', or 'UNDIRECTED'.
         relationship_types : list[str]
             The relationship types used to select relationships for this algorithm run

graphdatascience/procedure_surface/api/centrality/eigenvector_endpoints.py

@@ -20,7 +20,7 @@ def mutate(
         mutate_property: str,
         max_iterations: int = 20,
         tolerance: float = 1.0e-7,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
         scaler: str | dict[str, str | int | float] | ScalerConfig = "NONE",
         relationship_weight_property: str | None = None,
         relationship_types: list[str] = ALL_TYPES,
@@ -49,8 +49,11 @@ def mutate(
             The maximum number of iterations to run the algorithm
         tolerance : float
             The tolerance for convergence detection
-        source_nodes : Any | None, default=None
-            The source nodes to start the computation from
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
         scaler : str | dict[str, str | int | float] | ScalerConfig, default="NONE"
             The scaler to use. Can be:
             - A string (e.g., 'MinMax', 'Mean', 'Max', 'Log', 'StdScore', 'Center', 'NONE')
@@ -88,7 +91,7 @@ def stats(
         G: GraphV2,
         max_iterations: int = 20,
         tolerance: float = 1.0e-7,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
         scaler: str | dict[str, str | int | float] | ScalerConfig = "NONE",
         relationship_weight_property: str | None = None,
         relationship_types: list[str] = ALL_TYPES,
@@ -115,8 +118,11 @@ def stats(
             The maximum number of iterations to run the algorithm
         tolerance : float
             The tolerance for convergence detection
-        source_nodes : Any | None, default=None
-            The source nodes to start the computation from
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
         scaler : str | dict[str, str | int | float] | ScalerConfig, default="NONE"
             The scaler to use. Can be:
             - A string (e.g., 'MinMax', 'Mean', 'Max', 'Log', 'StdScore', 'Center', 'NONE')
@@ -154,7 +160,7 @@ def stream(
         G: GraphV2,
         max_iterations: int = 20,
         tolerance: float = 1.0e-7,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
         scaler: str | dict[str, str | int | float] | ScalerConfig = "NONE",
         relationship_weight_property: str | None = None,
         relationship_types: list[str] = ALL_TYPES,
@@ -176,8 +182,11 @@ def stream(
             The maximum number of iterations to run the algorithm
         tolerance : float
             The tolerance for convergence detection
-        source_nodes : Any | None, default=None
-            The source nodes to start the computation from
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
         scaler : str | dict[str, str | int | float] | ScalerConfig, default="NONE"
             The scaler to use. Can be:
             - A string (e.g., 'MinMax', 'Mean', 'Max', 'Log', 'StdScore', 'Center', 'NONE')
@@ -216,7 +225,7 @@ def write(
         write_property: str,
         max_iterations: int = 20,
         tolerance: float = 1.0e-7,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
         scaler: str | dict[str, str | int | float] | ScalerConfig = "NONE",
         relationship_weight_property: str | None = None,
         relationship_types: list[str] = ALL_TYPES,
@@ -246,8 +255,11 @@ def write(
             The maximum number of iterations to run the algorithm
         tolerance : float
             The tolerance for convergence detection
-        source_nodes : Any | None, default=None
-            The source nodes to start the computation from
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
         scaler : str | dict[str, str | int | float] | ScalerConfig, default="NONE"
             The scaler to use. Can be:
             - A string (e.g., 'MinMax', 'Mean', 'Max', 'Log', 'StdScore', 'Center', 'NONE')
@@ -287,7 +299,7 @@ def estimate(
         G: GraphV2 | dict[str, Any],
         max_iterations: int = 20,
         tolerance: float = 1.0e-7,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
         scaler: str | dict[str, str | int | float] | ScalerConfig = "NONE",
         relationship_weight_property: str | None = None,
         relationship_types: list[str] = ALL_TYPES,
@@ -305,8 +317,11 @@ def estimate(
             The maximum number of iterations to run the algorithm
         tolerance : float
             The tolerance for convergence detection
-        source_nodes : Any | None, default=None
-            The source nodes to start the computation from
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
         scaler : str | dict[str, str | int | float] | ScalerConfig, default="NONE"
             The scaler to use. Can be:
             - A string (e.g., 'MinMax', 'Mean', 'Max', 'Log', 'StdScore', 'Center', 'NONE')

graphdatascience/procedure_surface/api/centrality/pagerank_endpoints.py

@@ -30,7 +30,7 @@ def mutate(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> PageRankMutateResult:
         """
         Runs the PageRank algorithm and stores the results in the graph catalog as a new node property.
@@ -72,8 +72,11 @@ def mutate(
             Identifier for the job.
         relationship_weight_property : str | None, default=None
             Name of the property to be used as weights.
-        source_nodes : Any | None, default=None
-            List of node ids to use as starting points. Use a list of list pairs to associate each node with a bias > 0.
+        source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
 
         Returns
         -------
@@ -98,7 +101,7 @@ def stats(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> PageRankStatsResult:
         """
         Runs the PageRank algorithm and returns result statistics without storing the results.
@@ -138,8 +141,11 @@ def stats(
             Identifier for the job.
         relationship_weight_property : str | None, default=None
             Name of the property to be used as weights.
-        source_nodes : Any | None, default=None
-            List of node ids to use as starting points. Use a list of list pairs to associate each node with a bias > 0.
+                source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
 
         Returns
         -------
@@ -164,7 +170,7 @@ def stream(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> DataFrame:
         """
         Executes the PageRank algorithm and returns a stream of results.
@@ -228,7 +234,7 @@ def write(
         concurrency: int | None = None,
         job_id: str | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
         write_concurrency: int | None = None,
     ) -> PageRankWriteResult:
         """
@@ -271,8 +277,11 @@ def write(
             Identifier for the job.
         relationship_weight_property : str | None, default=None
             Name of the property to be used as weights.
-        source_nodes : Any | None, default=None
-            List of node ids to use as starting points. Use a list of list pairs to associate each node with a bias > 0.
+                source_nodes : int | list[int] | list[tuple[int, float]] | None, default=None
+            node ids to use as starting points. Can be:
+            - single node id (e.g., 42)
+            - list of node id (e.g., [42, 43, 44])
+            - list of tuples to associate each node with a bias > 0 (e.g., [(42, 0.5), (43, 1.0)])
         write_concurrency : int | None, default=None
             The number of concurrent threads used for writing
 
@@ -295,7 +304,7 @@ def estimate(
         node_labels: list[str] = ALL_LABELS,
         concurrency: int | None = None,
         relationship_weight_property: str | None = None,
-        source_nodes: Any | None = None,
+        source_nodes: int | list[int] | list[tuple[int, float]] | None = None,
     ) -> EstimationResult:
         """
         Estimate the memory consumption of an algorithm run.