+ jt^RIt^RIHtHtR.t]!R4]!^4]P !RR7RRl444tR#) N)not_implemented_forpy_random_stateaverage_clusteringdirectedapproximate_average_clustering)namecx\V4p^p\V4p\V4Uu.uF#p\VP 4V,4NK% upFYp\WV,,4p\V4^8dK-VP V^4wrWV ,9gKPV^, pK[ WA, #uupi)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 )lenlistrangeintrandomsample) Gtrialsseedn trianglesnodesinbrsuvs &&& y/home/luca/screen-checking/.venv/lib/python3.14/site-packages/networkx/algorithms/approximation/clustering_coefficient.pyrrsl AAI GE.3Fm r"sM?  Z 78=9!=r!