node: Producer
    max_levels: int = 4,
    max_sweeps: int = 8,
    level_tolerance: float = 0.01,
    sweep_tolerance: float = 0.0001,
    teleportation_rate: float = 0.15,
    visit_rate_tolerance: float = 1e-15,
    randomization_seed: int | None = None,
) -> Expression

Assign a community label to node using the Infomap algorithm. Infomap identifies communities using tools from information theory to find the most compact way to encode the network structure using the map equation. Nodes assigned to the same community are more likely to interact or communicate with one another as information flows through the network. Must be called in a rule or query context.

Supported Graph Types#

Graph TypeSupportedNotes
WeightedYesOnly positive weights are supported.


nodeProducerA node in the graph.
max_levelsintThe maximum number of levels at which to optimize. Default is 4. Must be positive.
max_sweepsintThe maximum number of iterations to run at each level. Default is 8. Must be non-negative.
level_tolerancefloatThe minimum change in the map equation required to continue to the next level. Default is 0.01. Must be non-negative.
sweep_tolerancefloatThe minimum change in the map equation required to continue to the next sweep. Default is 0.0001. Must be non-negative.
teleportation_ratefloatThe probability of teleporting to a random node. Default is 0.15. Must be in the range (0, 1].
visit_rate_tolerancefloatThe minimum change in the visit rate required to continue to the next sweep. Default is 1e-15. Must be non-negative.
randomization_seedint or NoneThe seed for the algorithm’s random number generator. Default is None. Must be non-negative.


Returns an Expression object that produces the integer community label assigned by Infomap to node as an integer value.


Use .infomap() to assign community labels to nodes in a graph using the Infomap algorithm. You access the .infomap() 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")
    carol = Person.add(name="Carol")
    daniel = Person.add(name="Daniel")
    evelyn = Person.add(name="Evelyn")

# 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 Infomap algorithm.
with model.query() as select:
    community = graph.compute.infomap(Person(name="Alice"))
    response = select(alias(community, "community_label"))

# Output:
#    community_label
# 0                2

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

# Output:
#      name  community_label
# 0   Alice                2
# 1     Bob                2
# 2   Carol                1
# 3  Daniel                1

In this example, .infomap() finds two communities in the graph: Alice and Bob are in one community, and Carol and Daniel are in another. Note that isolated nodes, like Evelyn, are not assigned a community ID and are filtered from the query.

See Also#