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 is the fraction of pairs of the node’s neighbors that are connected to another neighbor. 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.

Directed graphs are not supported.

Supported Graph Types#

Graph TypeSupportedNotes
DirectedNo
UndirectedYes
WeightedYesWeights are ignored.
UnweightedYes

Parameters#

NameTypeDescription
`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.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
``````