Source code for networkx.algorithms.approximation.clustering_coefficient

import networkx as nx
from networkx.utils import not_implemented_for, py_random_state

__all__ = ["average_clustering"]

[docs] @not_implemented_for("directed") @py_random_state(2) @nx._dispatchable(name="approximate_average_clustering") 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 Raises ------ NetworkXNotImplemented If G is directed. References ---------- .. [1] Schank, Thomas, and Dorothea Wagner. Approximating clustering coefficient and transitivity. Universität Karlsruhe, Fakultät für Informatik, 2004. """ 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