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Add support for sllpa in v2
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‎graphdatascience/procedure_surface/api/community/sllpa_endpoints.py‎

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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any, List, Optional, Union
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from pandas import DataFrame
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from graphdatascience.procedure_surface.api.base_result import BaseResult
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from graphdatascience.procedure_surface.api.catalog.graph_api import GraphV2
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from graphdatascience.procedure_surface.api.estimation_result import EstimationResult
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class SllpaEndpoints(ABC):
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@abstractmethod
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def mutate(
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self,
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G: GraphV2,
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mutate_property: str,
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*,
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max_iterations: int,
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concurrency: Optional[int] = None,
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job_id: Optional[str] = None,
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log_progress: bool = True,
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min_association_strength: Optional[float] = None,
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node_labels: Optional[List[str]] = None,
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partitioning: Optional[Any] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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username: Optional[str] = None,
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) -> SllpaMutateResult:
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"""
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Executes the Speaker-Listener Label Propagation algorithm (SLLPA) and writes the results to the in-memory graph as node properties.
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Parameters
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----------
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G : GraphV2
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The graph to run the algorithm on
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mutate_property : str
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The property name to store the community ID for each node
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max_iterations : int
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Maximum number of iterations for the algorithm
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concurrency : Optional[int], default=None
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The number of concurrent threads
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job_id : Optional[str], default=None
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An identifier for the job
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log_progress : bool, default=True
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Whether to log progress
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min_association_strength : Optional[float], default=None
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Minimum association strength for community assignment
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node_labels : Optional[List[str]], default=None
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Filter nodes with specific labels
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partitioning : Optional[Any], default=None
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Partitioning configuration for the algorithm
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relationship_types : Optional[List[str]], default=None
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Filter relationships with specific types
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sudo : Optional[bool], default=False
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Run with elevated privileges
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username : Optional[str], default=None
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Username for authentication
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Returns
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-------
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SllpaMutateResult
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An object containing metadata about the algorithm execution and the mutation
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"""
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...
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@abstractmethod
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def stats(
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self,
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G: GraphV2,
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*,
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max_iterations: int,
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concurrency: Optional[int] = None,
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job_id: Optional[str] = None,
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log_progress: bool = True,
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min_association_strength: Optional[float] = None,
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node_labels: Optional[List[str]] = None,
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partitioning: Optional[Any] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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username: Optional[str] = None,
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) -> SllpaStatsResult:
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"""
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Executes the Speaker-Listener Label Propagation algorithm (SLLPA) and returns statistics about the communities.
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Parameters
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----------
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G : GraphV2
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The graph to run the algorithm on
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concurrency : Optional[int], default=None
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The number of concurrent threads
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job_id : Optional[str], default=None
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An identifier for the job
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log_progress : bool, default=True
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Whether to log progress
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min_association_strength : Optional[float], default=None
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Minimum association strength for community assignment
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node_labels : Optional[List[str]], default=None
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Filter nodes with specific labels
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partitioning : Optional[Any], default=None
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Partitioning configuration for the algorithm
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relationship_types : Optional[List[str]], default=None
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Filter relationships with specific types
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sudo : Optional[bool], default=False
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Run with elevated privileges
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username : Optional[str], default=None
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Username for authentication
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Returns
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-------
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SllpaStatsResult
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An object containing statistics about the algorithm execution
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"""
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...
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@abstractmethod
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def stream(
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self,
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G: GraphV2,
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*,
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max_iterations: int,
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concurrency: Optional[int] = None,
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job_id: Optional[str] = None,
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log_progress: bool = True,
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min_association_strength: Optional[float] = None,
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node_labels: Optional[List[str]] = None,
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partitioning: Optional[Any] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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username: Optional[str] = None,
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) -> DataFrame:
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"""
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Executes the Speaker-Listener Label Propagation algorithm (SLLPA) and returns the results as a DataFrame.
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Parameters
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----------
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G : GraphV2
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The graph to run the algorithm on
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concurrency : Optional[int], default=None
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The number of concurrent threads
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job_id : Optional[str], default=None
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An identifier for the job
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log_progress : bool, default=True
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Whether to log progress
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min_association_strength : Optional[float], default=None
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Minimum association strength for community assignment
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node_labels : Optional[List[str]], default=None
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Filter nodes with specific labels
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partitioning : Optional[Any], default=None
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Partitioning configuration for the algorithm
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relationship_types : Optional[List[str]], default=None
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Filter relationships with specific types
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sudo : Optional[bool], default=False
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Run with elevated privileges
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username : Optional[str], default=None
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Username for authentication
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Returns
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-------
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DataFrame
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DataFrame containing node IDs and their community values
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"""
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...
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@abstractmethod
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def write(
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self,
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G: GraphV2,
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write_property: str,
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*,
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max_iterations: int,
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concurrency: Optional[int] = None,
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job_id: Optional[str] = None,
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log_progress: bool = True,
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min_association_strength: Optional[float] = None,
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node_labels: Optional[List[str]] = None,
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partitioning: Optional[Any] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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username: Optional[str] = None,
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write_concurrency: Optional[int] = None,
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) -> SllpaWriteResult:
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"""
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Executes the Speaker-Listener Label Propagation algorithm (SLLPA) and writes the results back to the database.
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Parameters
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----------
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G : GraphV2
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The graph to run the algorithm on
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write_property : str
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The property name to store the community ID for each node in the database
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concurrency : Optional[int], default=None
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The number of concurrent threads
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job_id : Optional[str], default=None
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An identifier for the job
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log_progress : bool, default=True
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Whether to log progress
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min_association_strength : Optional[float], default=None
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Minimum association strength for community assignment
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node_labels : Optional[List[str]], default=None
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Filter nodes with specific labels
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partitioning : Optional[Any], default=None
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Partitioning configuration for the algorithm
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relationship_types : Optional[List[str]], default=None
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Filter relationships with specific types
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sudo : Optional[bool], default=False
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Run with elevated privileges
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username : Optional[str], default=None
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Username for authentication
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write_concurrency : Optional[int], default=None
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The number of concurrent threads for writing
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Returns
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-------
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SllpaWriteResult
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An object containing metadata about the algorithm execution and the write operation
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"""
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...
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@abstractmethod
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def estimate(
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self,
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G: Union[GraphV2, dict[str, Any]],
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*,
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max_iterations: int,
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concurrency: Optional[int] = None,
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min_association_strength: Optional[float] = None,
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node_labels: Optional[List[str]] = None,
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partitioning: Optional[Any] = None,
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relationship_types: Optional[List[str]] = None,
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) -> EstimationResult:
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"""
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Estimates the memory consumption for running the Speaker-Listener Label Propagation algorithm (SLLPA).
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Parameters
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----------
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G : Union[GraphV2, dict[str, Any]]
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The graph to estimate for, or a graph configuration dictionary
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concurrency : Optional[int], default=None
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The number of concurrent threads
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min_association_strength : Optional[float], default=None
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Minimum association strength for community assignment
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node_labels : Optional[List[str]], default=None
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Filter nodes with specific labels
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partitioning : Optional[Any], default=None
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Partitioning configuration for the algorithm
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relationship_types : Optional[List[str]], default=None
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Filter relationships with specific types
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Returns
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-------
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EstimationResult
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An object containing the memory estimation
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"""
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...
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class SllpaMutateResult(BaseResult):
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"""
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Represents the result of the Speaker-Listener Label Propagation algorithm (SLLPA) mutate operation.
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Attributes
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----------
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ran_iterations : int
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The number of iterations the algorithm ran
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did_converge : bool
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Whether the algorithm converged
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pre_processing_millis : int
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Time spent preprocessing in milliseconds
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compute_millis : int
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Time spent computing in milliseconds
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mutate_millis : int
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Time spent writing results to the graph in milliseconds
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node_properties_written : int
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The number of node properties written
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configuration : dict
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The configuration used for the algorithm
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"""
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ran_iterations: int
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did_converge: bool
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pre_processing_millis: int
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compute_millis: int
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mutate_millis: int
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node_properties_written: int
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configuration: dict[str, Any]
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class SllpaStatsResult(BaseResult):
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"""
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Represents the result of the Speaker-Listener Label Propagation algorithm (SLLPA) stats operation.
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Attributes
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----------
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ran_iterations : int
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The number of iterations the algorithm ran
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did_converge : bool
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Whether the algorithm converged
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pre_processing_millis : int
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Time spent preprocessing in milliseconds
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compute_millis : int
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Time spent computing in milliseconds
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configuration : dict
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The configuration used for the algorithm
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"""
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ran_iterations: int
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did_converge: bool
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pre_processing_millis: int
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compute_millis: int
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configuration: dict[str, Any]
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class SllpaWriteResult(BaseResult):
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"""
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Represents the result of the Speaker-Listener Label Propagation algorithm (SLLPA) write operation.
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Attributes
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----------
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ran_iterations : int
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The number of iterations the algorithm ran
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did_converge : bool
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Whether the algorithm converged
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pre_processing_millis : int
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Time spent preprocessing in milliseconds
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compute_millis : int
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Time spent computing in milliseconds
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write_millis : int
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Time spent writing results to the database in milliseconds
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node_properties_written : int
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The number of node properties written
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configuration : dict
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The configuration used for the algorithm
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"""
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ran_iterations: int
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did_converge: bool
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pre_processing_millis: int
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compute_millis: int
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write_millis: int
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node_properties_written: int
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configuration: dict[str, Any]

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