triangle_community(node: Producer) -> Expression

Assign a community label to node using the percolation method. The percolation method finds communities of densely connected nodes by identifying triangles in the graph and iteratively merging triangles that share an edge until no more triangles can be merged. Nodes that are not part of any triangles are excluded. Must be called in a rule or query context.

Supported Graph Types#

Graph TypeSupportedNotes
WeightedYesWeights are ignored.


nodeProducerA node in the graph.


Returns an Expression object that produces the community label of node as an integer value.


Use .triangle_community() to assign a community label to a node in a graph. You access the .triangle_community() 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 `follows` property.
with model.rule():
    alice = Person.add(name="Alice")
    bob = Person.add(name="Bob")
    charlie = Person.add(name="Charlie")
    diana = Person.add(name="Diana")
    charlie.follows.extend([alice, diana])

# Create a directed graph with Person nodes and edges between followers.
# Note that graphs are directed by default.
graph = Graph(model)

# Find the community label for a single person using the percolation method.
with model.query() as select:
    community = graph.compute.triangle_community(Person(name="Alice"))
    response = select(alias(community, "community_label"))

# Output:
#    community_label
# 0                1

# Compute the community label for each person in the graph.
with model.query() as select:
    person = Person()
    community = graph.compute.triangle_community(person)
    response = select(, alias(community, "community_label"))

# Output:
#     name  community_label
# 0  Alice                1
# 1    Bob                1

The above graph has edges from Alice to Bob, Bob to Charlie, Charlie to Alice, and Charlie to Diana. Alice, Bob, and Charlie form a triangle, but Diana is not part of any triangles. The percolation method assigns Alice and Bob to the same community. Diana is filtered out because she is not part of any triangles.

Use std.aggregate.count() to count the number of communities identified in the graph:

from relationalai.std.aggregate import count

with model.query() as select:
    person = Person()
    community = graph.compute.triangle_community(person)
    response = select(alias(count(communities), "num_communities"))

# Output:
#   num_communities
# 0               1

See Also#