# avg_degree()#

relationalai.std.graphs.Compute
``````#avg_degree() -> Expression
``````

Get the average degree of all nodes the graph. For an undirected graph, the degree of a node is the number of neighbors it has in the graph. In a directed graph, the degree of a node is the sum of its indegree and outdegree. Must be called in a rule or query context.

## Supported Graph Types#

Graph TypeSupportedNotes
DirectedYes
UndirectedYes
WeightedYesWeights are ignored.
UnweightedYes

## Returns#

Returns an Expression object that produces the average degree of the graph as a floating-point value.

## Example#

Use `.avg_degree()` to compute the average degree of a graph:

``````#import relationalai as rai
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 graph and connect them with a 'friends' property.
with model.rule():
alice.friends.extend([bob, 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 Alice and Carol.
graph = Graph(model, undirected=True)
graph.Node.extend(Person)
graph.Edge.extend(Person.friends)

# Compute the average degree of the graph.
with model.query() as select:
avg_degree = graph.compute.avg_degree()
response = select(avg_degree)

print(response.results)
# Output:
#           v
# 0  1.333333
``````

In directed graphs, the degree of a node is the sum of its indegree and outdegree:

``````## Create a directed graph with Person  nodes and edges between friends.
# Note that graphs are directed by default.
# This graph has four edges: two between Alice and Bob and two between Alice and Carol.
directed_graph = Graph(model)
directed_graph.Node.extend(Person)
directed_graph.Edge.extend(Person.friends)

# Compute the average degree of the directed graph.
with model.query() as select:
avg_degree = directed_graph.compute.avg_degree()
response = select(avg_degree)

print(response.results)
# Output:
#           v
# 0  2.666667
``````