Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/algorithms/approximation/clustering_coefficient.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/algorithms/approximation/clustering_coefficient.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,58 @@ +from networkx.utils import not_implemented_for +from networkx.utils import py_random_state + +__all__ = ["average_clustering"] + + +@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. + + References + ---------- + .. [1] Schank, Thomas, and Dorothea Wagner. Approximating clustering + coefficient and transitivity. Universität Karlsruhe, Fakultät für + Informatik, 2004. + http://www.emis.ams.org/journals/JGAA/accepted/2005/SchankWagner2005.9.2.pdf + + """ + 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 / float(trials)