Compute.local_clustering_coefficient()#

relationalai.std.graphs
#Compute.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 TypeSupportedNotes
DirectedNoNot applicable.
UndirectedYes
WeightedYesWeights are ignored.
UnweightedYes

Parameters#

NameTypeDescription
nodeProducerA 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

See Also#