~it~SSKrSSKJrJr S/r\"S5\"S5\R "SS9S Sj555rg) N)not_implemented_forpy_random_stateaverage_clusteringdirectedapproximate_average_clustering)namecF[U5nSn[U5n[U5Vs/sHn[UR 5U-5PM! snHGn[XU5n[U5S:aM$UR US5upXU ;dMBUS- nMI XA- $s snf)u{Estimates the average clustering coefficient of G. The local clustering of each node in `G` is the fraction of triangles that actually exist over all possible triangles in its neighborhood. The average clustering coefficient of a graph `G` is the mean of local clusterings. This function finds an approximate average clustering coefficient for G by repeating `n` times (defined in `trials`) the following experiment: choose a node at random, choose two of its neighbors at random, and check if they are connected. The approximate coefficient is the fraction of triangles found over the number of trials [1]_. Parameters ---------- G : NetworkX graph trials : integer Number of trials to perform (default 1000). seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. Returns ------- c : float Approximated average clustering coefficient. Examples -------- >>> from networkx.algorithms import approximation >>> G = nx.erdos_renyi_graph(10, 0.2, seed=10) >>> approximation.average_clustering(G, trials=1000, seed=10) 0.214 Raises ------ NetworkXNotImplemented If G is directed. References ---------- .. [1] Schank, Thomas, and Dorothea Wagner. Approximating clustering coefficient and transitivity. Universität Karlsruhe, Fakultät für Informatik, 2004. https://doi.org/10.5445/IR/1000001239 rr)lenlistrangeintrandomsample) Gtrialsseedn trianglesnodesinbrsuvs u/home/mande/repo/quber/.venv/lib/python3.13/site-packages/networkx/algorithms/approximation/clustering_coefficient.pyrrsl AAI GE.3Fm r$sM?  Z 78=9!=r#