# Source code for networkx.algorithms.assortativity.neighbor_degree

```
import networkx as nx
__all__ = ["average_neighbor_degree"]
[docs]
@nx._dispatchable(edge_attrs="weight")
def average_neighbor_degree(G, source="out", target="out", nodes=None, weight=None):
r"""Returns the average degree of the neighborhood of each node.
In an undirected graph, the neighborhood `N(i)` of node `i` contains the
nodes that are connected to `i` by an edge.
For directed graphs, `N(i)` is defined according to the parameter `source`:
- if source is 'in', then `N(i)` consists of predecessors of node `i`.
- if source is 'out', then `N(i)` consists of successors of node `i`.
- if source is 'in+out', then `N(i)` is both predecessors and successors.
The average neighborhood degree of a node `i` is
.. math::
k_{nn,i} = \frac{1}{|N(i)|} \sum_{j \in N(i)} k_j
where `N(i)` are the neighbors of node `i` and `k_j` is
the degree of node `j` which belongs to `N(i)`. For weighted
graphs, an analogous measure can be defined [1]_,
.. math::
k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j
where `s_i` is the weighted degree of node `i`, `w_{ij}`
is the weight of the edge that links `i` and `j` and
`N(i)` are the neighbors of node `i`.
Parameters
----------
G : NetworkX graph
source : string ("in"|"out"|"in+out"), optional (default="out")
Directed graphs only.
Use "in"- or "out"-neighbors of source node.
target : string ("in"|"out"|"in+out"), optional (default="out")
Directed graphs only.
Use "in"- or "out"-degree for target node.
nodes : list or iterable, optional (default=G.nodes)
Compute neighbor degree only for specified nodes.
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used as a weight.
If None, then each edge has weight 1.
Returns
-------
d: dict
A dictionary keyed by node to the average degree of its neighbors.
Raises
------
NetworkXError
If either `source` or `target` are not one of 'in', 'out', or 'in+out'.
If either `source` or `target` is passed for an undirected graph.
Examples
--------
>>> G = nx.path_graph(4)
>>> G.edges[0, 1]["weight"] = 5
>>> G.edges[2, 3]["weight"] = 3
>>> nx.average_neighbor_degree(G)
{0: 2.0, 1: 1.5, 2: 1.5, 3: 2.0}
>>> nx.average_neighbor_degree(G, weight="weight")
{0: 2.0, 1: 1.1666666666666667, 2: 1.25, 3: 2.0}
>>> G = nx.DiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> nx.average_neighbor_degree(G, source="in", target="in")
{0: 0.0, 1: 0.0, 2: 1.0, 3: 1.0}
>>> nx.average_neighbor_degree(G, source="out", target="out")
{0: 1.0, 1: 1.0, 2: 0.0, 3: 0.0}
See Also
--------
average_degree_connectivity
References
----------
.. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
"The architecture of complex weighted networks".
PNAS 101 (11): 3747–3752 (2004).
"""
if G.is_directed():
if source == "in":
source_degree = G.in_degree
elif source == "out":
source_degree = G.out_degree
elif source == "in+out":
source_degree = G.degree
else:
raise nx.NetworkXError(
f"source argument {source} must be 'in', 'out' or 'in+out'"
)
if target == "in":
target_degree = G.in_degree
elif target == "out":
target_degree = G.out_degree
elif target == "in+out":
target_degree = G.degree
else:
raise nx.NetworkXError(
f"target argument {target} must be 'in', 'out' or 'in+out'"
)
else:
if source != "out" or target != "out":
raise nx.NetworkXError(
f"source and target arguments are only supported for directed graphs"
)
source_degree = target_degree = G.degree
# precompute target degrees -- should *not* be weighted degree
t_deg = dict(target_degree())
# Set up both predecessor and successor neighbor dicts leaving empty if not needed
G_P = G_S = {n: {} for n in G}
if G.is_directed():
# "in" or "in+out" cases: G_P contains predecessors
if "in" in source:
G_P = G.pred
# "out" or "in+out" cases: G_S contains successors
if "out" in source:
G_S = G.succ
else:
# undirected leave G_P empty but G_S is the adjacency
G_S = G.adj
# Main loop: Compute average degree of neighbors
avg = {}
for n, deg in source_degree(nodes, weight=weight):
# handle degree zero average
if deg == 0:
avg[n] = 0.0
continue
# we sum over both G_P and G_S, but one of the two is usually empty.
if weight is None:
avg[n] = (
sum(t_deg[nbr] for nbr in G_S[n]) + sum(t_deg[nbr] for nbr in G_P[n])
) / deg
else:
avg[n] = (
sum(dd.get(weight, 1) * t_deg[nbr] for nbr, dd in G_S[n].items())
+ sum(dd.get(weight, 1) * t_deg[nbr] for nbr, dd in G_P[n].items())
) / deg
return avg
```