Compute.ego_network() Preview #

relationalai.std.graphs

#Compute.ego_network(node: Producer,
                    hops: int = 1
) -> Expression

This method returns the induced subgraph containing all nodes within k undirected hops from a specified source node (ego-node). It identifies nearby nodes based on undirected shortest path distance, effectively capturing the local structure around the ego-node. This is useful for analyzing local connectivity patterns or extracting ego-networks. Must be called in a rule or query context.

Supported Graph Types#

Graph Type	Supported	Notes
Directed	Yes
Undirected	Yes
Weighted	Yes	Weights are ignored.
Unweighted	Yes

Returns#

Returns an Expression that produces the set of edges induced by the ego_network function.

Examples#

You access the .ego_network(node, hops) method from a Graph object’s .compute attribute:

#
import relationalai as rai
from relationalai.std import alias
from relationalai.std.graphs import Graph

model = rai.Model("socialNetwork")
Person = model.Type("Person")

with model.rule():
   alice = Person.add(name="Alice")
   bob = Person.add(name="Bob")
   claire = Person.add(name="Claire")
   dave = Person.add(name="Dave")
   eve = Person.add(name="Eve")
   frank = Person.add(name="Frank")
   gina = Person.add(name="Gina")
   alice.follows.add(alice)
   alice.follows.add(bob)
   bob.follows.add(claire)
   claire.follows.add(dave)
   dave.follows.add(eve)
   eve.follows.add(frank)
   frank.follows.add(gina)


graph = Graph(model, undirected=False)
graph.Node.extend(Person)
graph.Edge.extend(Person.follows)


with model.query() as select:
   node = Person()
   a,b = graph.compute.ego_network(node, 1)
   ego_response = select(alias(node.name, "ego-node") , alias( Person(a).name, "u"), alias(Person(b).name, "v"))

print(ego_response.results)

# output
#     ego-node   u       v
# 0    Alice   Alice   Alice
# 1    Alice   Alice     Bob
# 2      Bob   Alice   Alice
# 3      Bob   Alice     Bob
# 4      Bob     Bob  Claire
# 5   Claire     Bob  Claire
# 6   Claire  Claire    Dave
# 7     Dave  Claire    Dave
# 8     Dave    Dave     Eve
# 9      Eve    Dave     Eve
# 10     Eve     Eve   Frank
# 11   Frank     Eve   Frank
# 12   Frank   Frank    Gina
# 13    Gina   Frank    Gina

If you’re interested in retrieving the 2-hop ego-network of a specific node (e.g., Alice), you can apply the following modification to the code above:

#
with model.query() as select:
   node = Person(name="Alice")
   a,b = graph.compute.ego_network(node, hops=2)
   ego_response = select(alias(node.name, "ego-node") , alias( Person(a).name, "u"), alias(Person(b).name, "v"))

#     ego-node       u      v
# 0    Alice       Alice  Alice
# 1    Alice       Alice   Bob
# 2    Alice        Bob  Claire