weighted_degree_centrality(node: Producer) -> Expression

Compute the weighted degree centrality of a node in a graph. The weighted degree centrality of a node is the sum of the weights of the edges incident to the node divided by one less than the number of nodes in the graph. In unweighted graphs, .weighted_degree_centrality() is an alias of .degree_centrality(). Must be called in a rule or query context.

Supported Graph Types#

Graph TypeSupportedNotes
UnweightedYesEdge weights default to 1.0.


nodeProducerA node in the graph.


Returns an Expression object that produces the weighted degree centrality of the node as a floating-point value, calculated according to the formula:

weighted_degree_centrality = sum(weight of edges incident to node) / (number of nodes - 1)


Use .weighted_degree_centrality() to compute the weighted degree centrality of a node in a graph. You access the .weighted_degree_centrality() 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 Person and Friendship types.
model = rai.Model("socialNetwork")
Person = model.Type("Person")
Friendship = model.Type("Friendship")

# Add some people to the model and connect them with friendships.
with model.rule():
    alice = Person.add(name="Alice")
    bob = Person.add(name="Bob")
    carol = Person.add(name="Carol")
    Friendship.add(person1=alice, person2=bob, strength=100)
    Friendship.add(person1=bob, person2=carol, strength=10)

# Create an weighted, undirected graph with Person nodes and edges between friends.
# This graph has two edges: one from Alice and Bob and one from Bob and Carol.
# The edges are weighted by the strength of each friendship.
graph = Graph(model, undirected=True, weighted=True)
with model.rule():
    friendship = Friendship()
    graph.Edge.add(friendship.person1, friendship.person2, weight=friendship.strength)

# Compute the weighted degree centrality of each person in the graph.
with model.query() as select:
    person = Person()
    centrality = graph.compute.weighted_degree_centrality(person)
    response = select(person.name, alias(centrality, "weighted_degree_centrality"))

# Output:
#     name  weighted_degree_centrality
# 0  Alice                        50.0
# 1    Bob                        55.0
# 2  Carol                         5.0

See Also#