src.dackar.knowledge_graph.pygds

Created on March 20, 2025

@author: wangc, mandd

Attributes

logger

Classes

PyGDS

Module Contents

src.dackar.knowledge_graph.pygds.logger

class src.dackar.knowledge_graph.pygds.PyGDS(uri, user, pwd, database='neo4j')

__uri

__user

__pwd

__driver = None

__database = 'neo4j'

__graph = []

close()

Close the python neo4j GDS connection

restart()

Restart the python neo4j GDS connection

query(query, params=None, database=None)

User provided Cypher query statements for python neo4j driver to use to query database

Parameters:

• query (str) – user provided Cypher query statements

• parameters (dict, optional) – dictionary that provide key/value pairs for query statement to use. Defaults to None.

• db (str, optional) – name for database. Defaults to None.

Returns:

returned queried results.

Return type:

DataFrame

project(graph_name, node_spec, relationship_spec)

Creates a named graph in the catalog for use by algorithms

Parameters:

• graph_name (str) – graph name

• node_spec (str or dict) – Node project, dict option ({nodeLabel: {‘properties’:[properties]}})

• relationship_spec (str or dict) – Relationship projection, dict option ({relationLabel: {‘properties’:[properties]}})

Returns:

GDS graph object result (pandas.Series): containing metadata from underlying procedure call.

Return type:

graph (Graph object)

load_dataframe(graph_name, nodes, relationships, write=False)

Constructing a graph from pandas.DataFrames

Parameters:

• graph_name (str) – Name of the graph to be constructed

• nodes (pandas.DataFrame) – one or more dataframes containing node data

• relationships (pandas.DataFrame) – one or more dataframes containing relationship data

Returns:

GDS graph object

Return type:

graph (Graph object)

Examples

nodes = pandas.DataFrame(

{

“nodeId”: [0, 1, 2, 3], “labels”: [“A”, “B”, “C”, “A”], “prop1”: [42, 1337, 8, 0], “otherProperty”: [0.1, 0.2, 0.3, 0.4]

}

)

relationships = pandas.DataFrame(

{

“sourceNodeId”: [0, 1, 2, 3], “targetNodeId”: [1, 2, 3, 0], “relationshipType”: [“REL”, “REL”, “REL”, “REL”], “weight”: [0.0, 0.0, 0.1, 42.0]

}

)

check()

Print the graph information

reset()

Reset the GDS, delete the graph in the memory

centrality(method='eigenvector', check=False)

Centrality algorithms are used to understand the role or influence of particular nodes in a graph

Parameters:

• method (str, optional) – centrality algorithm. Defaults to ‘eigenvector’.

• connections ('Degree centrality' measures the number of)

• network. (to other nodes in the)

• intermediary ('Betweenness centrality' quantifies the importance of a node as a bridge or)

• nodes (in the network. It measures how often a node lies on the shortest path between other pairs of)

• connections

• check (bool, optional) – print graph information if True