local_clustering_coefficient()#
#local_clustering_coefficient(node: Producer) -> Expression
Compute the local clustering coefficient of a node in an undirected graph.
The local clustering coefficient of a node represents the proportion of
pairs among the node’s neighbors that are also connected to each other.
Values range from 0
to 1
, where 0
indicates none of the node’s neighbors are connected,
and 1
indicates that the node’s neighbors are fully connected, forming a clique.
Must be called in a rule or query context.
Supported Graph Types#
Graph Type | Supported | Notes |
---|---|---|
Directed | No | Not applicable. |
Undirected | Yes | |
Weighted | Yes | Weights are ignored. |
Unweighted | Yes |
Parameters#
Name | Type | Description |
---|---|---|
node | Producer | A node in the graph. |
Returns#
Returns an Expression object that produces
the local clustering coefficient of node
as a floating-point value, calculated by the following formula:
#local clustering coefficient = 2 * num_edges / (degree * (degree - 1))
Here, num_edges
is the total number of edges between the neighbors of node
and degree
is the degree of node
.
Values range from 0
to 1
, where 0
indicates none of the node’s neighbors are connected,
and 1
indicates that the node’s neighbors are fully connected, forming a clique.
Example#
Use .local_clustering_coefficient()
to compute the local clustering coefficient of a node in an undirected graph.
You access the .local_clustering_coefficient()
method from a Graph
object’s
.compute
attribute:
#import relationalai as rai
from relationalai.std import alias
from relationalai.std.graphs import Graph
# Create a model named "socialNetwork" with a Person type.
model = rai.Model("socialNetwork")
Person = model.Type("Person")
# Add some people to the model and connect them with a multi-valued `friend` property.
with model.rule():
alice = Person.add(name="Alice")
bob = Person.add(name="Bob")
carol = Person.add(name="Carol")
daniel = Person.add(name="Daniel")
alice.friends.extend([bob, carol])
bob.friends.extend([alice, carol, daniel])
# Create an undirected graph with Person nodes and edges between friends.
# This graph has four edges: Alice, Bob, and Carol form a triangle, and Daniel is only connected to Bob.
graph = Graph(model, undirected=True)
graph.Node.extend(Person)
graph.Edge.extend(Person.friends)
# Compute the local clustering coefficient of each person.
with model.query() as select:
person = Person()
lcc = graph.compute.local_clustering_coefficient(person)
response = select(person.name, alias(lcc, "clustering_coefficient"))
print(response.results)
# Output:
# name clustering_coefficient
# 0 Alice 1.000000
# 1 Bob 0.333333
# 2 Carol 1.000000
# 3 Daniel 0.000000
# Compute the local clustering coefficient of a specific person.
with model.query() as select:
lcc = graph.compute.local_clustering_coefficient(Person(name="Alice"))
response = select(alias(lcc, "clustering_coefficient"))
print(response.results)
# Output:
# clustering_coefficient
# 0 1.0