wgdZddlZddlZgdZdZdZdZeeedZejddZ e Z ejd dd Z ejd Z d ZdZy)z+Functions for computing clustering of pairsN) clusteringaverage_clusteringlatapy_clusteringrobins_alexander_clusteringc<t||zt||zz SN)lennunvs j/home/mcse/projects/flask/flask-venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/cluster.pycc_dotrs rBw<#b2g, &&c\t||ztt|t|z Sr)r maxr s r cc_maxr$ rBw<#c"gs2w/ //rc\t||ztt|t|z Sr)r minr s r cc_minrrr)dotrrc tjjj|stjd t |}||}i}|D]j}d}||D chc]}||D]} | c} }|hz } | D]&} ||t|| t||z }(|dkDr|t| z}|||<l|S#t $r}tjd|d}~wwxYwcc} }w)uCompute a bipartite clustering coefficient for nodes. The bipartite clustering coefficient is a measure of local density of connections defined as [1]_: .. math:: c_u = \frac{\sum_{v \in N(N(u))} c_{uv} }{|N(N(u))|} where `N(N(u))` are the second order neighbors of `u` in `G` excluding `u`, and `c_{uv}` is the pairwise clustering coefficient between nodes `u` and `v`. The mode selects the function for `c_{uv}` which can be: `dot`: .. math:: c_{uv}=\frac{|N(u)\cap N(v)|}{|N(u) \cup N(v)|} `min`: .. math:: c_{uv}=\frac{|N(u)\cap N(v)|}{min(|N(u)|,|N(v)|)} `max`: .. math:: c_{uv}=\frac{|N(u)\cap N(v)|}{max(|N(u)|,|N(v)|)} Parameters ---------- G : graph A bipartite graph nodes : list or iterable (optional) Compute bipartite clustering for these nodes. The default is all nodes in G. mode : string The pairwise bipartite clustering method to be used in the computation. It must be "dot", "max", or "min". Returns ------- clustering : dictionary A dictionary keyed by node with the clustering coefficient value. Examples -------- >>> from networkx.algorithms import bipartite >>> G = nx.path_graph(4) # path graphs are bipartite >>> c = bipartite.clustering(G) >>> c[0] 0.5 >>> c = bipartite.clustering(G, mode="min") >>> c[0] 1.0 See Also -------- robins_alexander_clustering average_clustering networkx.algorithms.cluster.square_clustering References ---------- .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008). Basic notions for the analysis of large two-mode networks. Social Networks 30(1), 31--48. zGraph is not bipartitez6Mode for bipartite clustering must be: dot, min or maxNg) nx algorithms bipartite is_bipartite NetworkXErrormodesKeyErrorsetr ) Gnodesmodecc_funcerrccsvccnbrunbrs2s r rrs\ == " " / / 2788+  } C  d3sAcF3q33qc9 0A '#ad)S1Y/ /B 0 8 #e* BA J!  D  4s CC' C$ CC$bipartite_average_clustering)namech||}t|||tfd|Dt|z S)u|Compute the average bipartite clustering coefficient. A clustering coefficient for the whole graph is the average, .. math:: C = \frac{1}{n}\sum_{v \in G} c_v, where `n` is the number of nodes in `G`. Similar measures for the two bipartite sets can be defined [1]_ .. math:: C_X = \frac{1}{|X|}\sum_{v \in X} c_v, where `X` is a bipartite set of `G`. Parameters ---------- G : graph a bipartite graph nodes : list or iterable, optional A container of nodes to use in computing the average. The nodes should be either the entire graph (the default) or one of the bipartite sets. mode : string The pairwise bipartite clustering method. It must be "dot", "max", or "min" Returns ------- clustering : float The average bipartite clustering for the given set of nodes or the entire graph if no nodes are specified. Examples -------- >>> from networkx.algorithms import bipartite >>> G = nx.star_graph(3) # star graphs are bipartite >>> bipartite.average_clustering(G) 0.75 >>> X, Y = bipartite.sets(G) >>> bipartite.average_clustering(G, X) 0.0 >>> bipartite.average_clustering(G, Y) 1.0 See Also -------- clustering Notes ----- The container of nodes passed to this function must contain all of the nodes in one of the bipartite sets ("top" or "bottom") in order to compute the correct average bipartite clustering coefficients. See :mod:`bipartite documentation ` for further details on how bipartite graphs are handled in NetworkX. References ---------- .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008). Basic notions for the analysis of large two-mode networks. Social Networks 30(1), 31--48. )r"r#c3(K|] }| ywr).0r'r&s r z%average_clustering..s%!s1v%s)rsumr )r!r"r#r&s @r rrs8N } AU 6C %u% %E 22rc|jdks|jdkryt|}|dk(ryt|}d|z|z S)u Compute the bipartite clustering of G. Robins and Alexander [1]_ defined bipartite clustering coefficient as four times the number of four cycles `C_4` divided by the number of three paths `L_3` in a bipartite graph: .. math:: CC_4 = \frac{4 * C_4}{L_3} Parameters ---------- G : graph a bipartite graph Returns ------- clustering : float The Robins and Alexander bipartite clustering for the input graph. Examples -------- >>> from networkx.algorithms import bipartite >>> G = nx.davis_southern_women_graph() >>> print(round(bipartite.robins_alexander_clustering(G), 3)) 0.468 See Also -------- latapy_clustering networkx.algorithms.cluster.square_clustering References ---------- .. [1] Robins, G. and M. Alexander (2004). Small worlds among interlocking directors: Network structure and distance in bipartite graphs. 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