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FlorentinDFlorentinD
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Add local clustering coefficient to v2 endpoints
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‎graphdatascience/procedure_surface/api/community/local_clustering_coefficient_endpoints.py‎

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from abc import abstractmethod
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from typing import Any, Dict, List, Optional
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import pandas as pd
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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 LocalClusteringCoefficientEndpoints:
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"""
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Interface for LocalClusteringCoefficient algorithm endpoints.
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"""
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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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*,
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mutate_property: str,
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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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node_labels: Optional[List[str]] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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triangle_count_property: Optional[str] = None,
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username: Optional[str] = None,
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) -> "LocalClusteringCoefficientMutateResult":
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"""
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Executes the LocalClusteringCoefficient algorithm and writes results back to the graph.
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Parameters
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----------
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G : GraphV2
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The graph on which to run the algorithm
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mutate_property : str
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Property name to store the result
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concurrency : Optional[int], default=None
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Number of concurrent threads
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job_id : Optional[str], default=None
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Job identifier for tracking
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log_progress : bool, default=True
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Whether to log progress
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node_labels : Optional[List[str]], default=None
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Node labels to include in the computation
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relationship_types : Optional[List[str]], default=None
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Relationship types to include in the computation
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sudo : Optional[bool], default=False
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Run with elevated privileges
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triangle_count_property : Optional[str], default=None
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Property name for pre-computed triangle counts
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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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LocalClusteringCoefficientMutateResult
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Result containing clustering coefficient statistics and timing information
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"""
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pass
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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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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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node_labels: Optional[List[str]] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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triangle_count_property: Optional[str] = None,
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username: Optional[str] = None,
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) -> "LocalClusteringCoefficientStatsResult":
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"""
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Executes the LocalClusteringCoefficient algorithm and returns statistics.
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Parameters
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----------
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G : GraphV2
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The graph on which to run the algorithm
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concurrency : Optional[int], default=None
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Number of concurrent threads
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job_id : Optional[str], default=None
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Job identifier for tracking
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log_progress : bool, default=True
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Whether to log progress
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node_labels : Optional[List[str]], default=None
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Node labels to include in the computation
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relationship_types : Optional[List[str]], default=None
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Relationship types to include in the computation
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sudo : Optional[bool], default=False
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Run with elevated privileges
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triangle_count_property : Optional[str], default=None
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Property name for pre-computed triangle counts
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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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LocalClusteringCoefficientStatsResult
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Result containing clustering coefficient statistics and timing information
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"""
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pass
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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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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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node_labels: Optional[List[str]] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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triangle_count_property: Optional[str] = None,
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username: Optional[str] = None,
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) -> pd.DataFrame:
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"""
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Executes the LocalClusteringCoefficient algorithm and streams results.
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Parameters
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----------
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G : GraphV2
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The graph on which to run the algorithm
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concurrency : Optional[int], default=None
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Number of concurrent threads
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job_id : Optional[str], default=None
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Job identifier for tracking
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log_progress : bool, default=True
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Whether to log progress
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node_labels : Optional[List[str]], default=None
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Node labels to include in the computation
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relationship_types : Optional[List[str]], default=None
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Relationship types to include in the computation
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sudo : Optional[bool], default=False
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Run with elevated privileges
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triangle_count_property : Optional[str], default=None
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Property name for pre-computed triangle counts
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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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pandas.DataFrame
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DataFrame containing nodeId and localClusteringCoefficient columns
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"""
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pass
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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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*,
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write_property: str,
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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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node_labels: Optional[List[str]] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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triangle_count_property: Optional[str] = None,
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username: Optional[str] = None,
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write_concurrency: Optional[int] = None,
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write_to_result_store: Optional[bool] = None,
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) -> "LocalClusteringCoefficientWriteResult":
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"""
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Executes the LocalClusteringCoefficient algorithm and writes results to the database.
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Parameters
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----------
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G : GraphV2
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The graph on which to run the algorithm
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write_property : str
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Property name to store results in the database
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concurrency : Optional[int], default=None
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Number of concurrent threads
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job_id : Optional[str], default=None
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Job identifier for tracking
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log_progress : bool, default=True
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Whether to log progress
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node_labels : Optional[List[str]], default=None
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Node labels to include in the computation
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relationship_types : Optional[List[str]], default=None
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Relationship types to include in the computation
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sudo : Optional[bool], default=False
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Run with elevated privileges
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triangle_count_property : Optional[str], default=None
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Property name for pre-computed triangle counts
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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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Concurrency for writing back to the database
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write_to_result_store : Optional[bool], default=None
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Whether to write to the result store
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Returns
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-------
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LocalClusteringCoefficientWriteResult
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Result containing clustering coefficient statistics and timing information
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"""
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pass
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@abstractmethod
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def estimate(
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self,
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G: GraphV2,
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*,
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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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node_labels: Optional[List[str]] = None,
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relationship_types: Optional[List[str]] = None,
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sudo: Optional[bool] = False,
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triangle_count_property: Optional[str] = None,
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username: Optional[str] = None,
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) -> EstimationResult:
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"""
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Estimates the LocalClusteringCoefficient algorithm memory requirements.
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Parameters
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----------
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G : GraphV2
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The graph on which to run the algorithm
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concurrency : Optional[int], default=None
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Number of concurrent threads
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job_id : Optional[str], default=None
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Job identifier for tracking
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log_progress : bool, default=True
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Whether to log progress
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node_labels : Optional[List[str]], default=None
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Node labels to include in the computation
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relationship_types : Optional[List[str]], default=None
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Relationship types to include in the computation
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sudo : Optional[bool], default=False
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Run with elevated privileges
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triangle_count_property : Optional[str], default=None
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Property name for pre-computed triangle counts
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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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EstimationResult
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Memory estimation details
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"""
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pass
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class LocalClusteringCoefficientMutateResult(BaseResult):
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pre_processing_millis: int
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compute_millis: int
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post_processing_millis: int
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mutate_millis: int
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node_count: int
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node_properties_written: int
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average_clustering_coefficient: float
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configuration: Dict[str, Any]
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class LocalClusteringCoefficientStatsResult(BaseResult):
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pre_processing_millis: int
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compute_millis: int
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post_processing_millis: int
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node_count: int
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average_clustering_coefficient: float
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configuration: Dict[str, Any]
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class LocalClusteringCoefficientWriteResult(BaseResult):
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pre_processing_millis: int
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compute_millis: int
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post_processing_millis: int
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write_millis: int
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node_count: int
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node_properties_written: int
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average_clustering_coefficient: float
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configuration: Dict[str, Any]

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