# Source code for networkx.algorithms.approximation.clustering_coefficient

```from networkx.utils import not_implemented_for, py_random_state

__all__ = ["average_clustering"]

[docs]@py_random_state(2)
@not_implemented_for("directed")
def average_clustering(G, trials=1000, seed=None):
r"""Estimates the average clustering coefficient of G.

The local clustering of each node in `G` is the fraction of triangles
that actually exist over all possible triangles in its neighborhood.
The average clustering coefficient of a graph `G` is the mean of
local clusterings.

This function finds an approximate average clustering coefficient
for G by repeating `n` times (defined in `trials`) the following
experiment: choose a node at random, choose two of its neighbors
at random, and check if they are connected. The approximate
coefficient is the fraction of triangles found over the number
of trials [1]_.

Parameters
----------
G : NetworkX graph

trials : integer
Number of trials to perform (default 1000).

seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.

Returns
-------
c : float
Approximated average clustering coefficient.

Examples
--------
>>> from networkx.algorithms import approximation
>>> G = nx.erdos_renyi_graph(10, 0.2, seed=10)
>>> approximation.average_clustering(G, trials=1000, seed=10)
0.214

References
----------
.. [1] Schank, Thomas, and Dorothea Wagner. Approximating clustering
coefficient and transitivity. Universität Karlsruhe, Fakultät für
Informatik, 2004.
https://doi.org/10.5445/IR/1000001239

"""
n = len(G)
triangles = 0
nodes = list(G)
for i in [int(seed.random() * n) for i in range(trials)]:
nbrs = list(G[nodes[i]])
if len(nbrs) < 2:
continue
u, v = seed.sample(nbrs, 2)
if u in G[v]:
triangles += 1
return triangles / trials
```