jaccard_similarity(node1: Producer, node2: Producer) -> Expression

Compute the Jaccard similarity between node1 and node2. Jaccard similarity measures the similarity between two nodes based on how many neighbors they share. Values range from 0.0 to 1.0, inclusive, where 1.0 indicates that the nodes have identical neighborhoods. Pairs of nodes with a similarity of 0.0, indicating no meaningful relationship, are excluded from results for improved performance. Must be called in a rule or query context.

Supported Graph Types#

Graph TypeSupportedNotes
WeightedYesWeights are ignored. Use .weighted_jaccard_similarity to take weights into account.


node1ProducerA node in the graph.
node2ProducerA node in the graph.


Returns an Expression object that produces the Jaccard similarity between the two nodes as a floating-point value, calculated by the following formula:

Jaccard similarity = (number of shared neighbors) / (total number of unique neighbors)


Use .jaccard_similarity() to compute the Jaccard similarity between two nodes in a graph. You access the .jaccard_similarity() 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")

# Create an undirected graph with Person nodes and edges between friends.
# This graph has two edges: one between Alice and Bob, and one between Bob and Carol.
graph = Graph(model, undirected=True)

with model.query() as select:
    # Get pairs of people.
    person1, person2 = Person(), Person()
    # Compute the Jaccard similarity between each pair of people.
    similarity = graph.compute.jaccard_similarity(person1, person2)
    # Select each person's name and their similarity value.
    response = select(person1.name, person2.name, alias(similarity, "jaccard_similarity"))

# Output:
#     name  name2  jaccard_similarity
# 0  Alice  Alice                 1.0
# 1  Alice  Carol                 1.0
# 2    Bob    Bob                 1.0
# 3  Carol  Alice                 1.0
# 4  Carol  Carol                 1.0

There is no row for Alice and Bob in the preceding query’s results. That’s because Alice and Bob have a Jaccard similarity of 0.0. Pairs of nodes with zero similarity, indicating no meaningful similarity, are often excluded from analyses. Consequently, we filter out these pairs to improve performance.

If node1 or node2 is not a node in the graph, no exception is raised. Instead, that object is filtered from the rule or query:

# Add a Company type to the model.
Company = model.Type("Company")

# Add some companies to the model.
with model.rule():
    apple = Company.add(name="Apple")
    google = Company.add(name="Google")

# Create the union of the Person and Company types.
PersonOrCompany = Person | Company

with model.query() as select:
    # Get all person and company objects.
    obj1, obj2 = PersonOrCompany(), PersonOrCompany()
    obj1 < obj2  # Ensure pairs are unique. Compares internal object IDs.
    # Compute the Jaccard similarity between each pair of objects.
    # Objects that are not nodes in the graph are filtered out of the results.
    similarity = graph.compute.jaccard_similarity(obj1, obj2)
    response = select(obj1.name, obj2.name, alias(similarity, "jaccard_similarity"))

# Only rows for people are returned, since companies are not nodes in the graph.
# Output:
#     name  name2  jaccard_similarity
# 0  Carol  Alice                 1.0

See Also#