## Mercurial > repos > shellac > sam_consensus_v3

### view env/lib/python3.9/site-packages/networkx/algorithms/centrality/current_flow_closeness.py @ 0:4f3585e2f14b draft default tip

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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |

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"""Current-flow closeness centrality measures.""" import networkx as nx from networkx.utils import not_implemented_for, reverse_cuthill_mckee_ordering from networkx.algorithms.centrality.flow_matrix import ( CGInverseLaplacian, FullInverseLaplacian, laplacian_sparse_matrix, SuperLUInverseLaplacian, ) __all__ = ["current_flow_closeness_centrality", "information_centrality"] @not_implemented_for("directed") def current_flow_closeness_centrality(G, weight=None, dtype=float, solver="lu"): """Compute current-flow closeness centrality for nodes. Current-flow closeness centrality is variant of closeness centrality based on effective resistance between nodes in a network. This metric is also known as information centrality. Parameters ---------- G : graph A NetworkX graph. weight : None or string, optional (default=None) If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight. dtype: data type (default=float) Default data type for internal matrices. Set to np.float32 for lower memory consumption. solver: string (default='lu') Type of linear solver to use for computing the flow matrix. Options are "full" (uses most memory), "lu" (recommended), and "cg" (uses least memory). Returns ------- nodes : dictionary Dictionary of nodes with current flow closeness centrality as the value. See Also -------- closeness_centrality Notes ----- The algorithm is from Brandes [1]_. See also [2]_ for the original definition of information centrality. References ---------- .. [1] Ulrik Brandes and Daniel Fleischer, Centrality Measures Based on Current Flow. Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05). LNCS 3404, pp. 533-544. Springer-Verlag, 2005. http://algo.uni-konstanz.de/publications/bf-cmbcf-05.pdf .. [2] Karen Stephenson and Marvin Zelen: Rethinking centrality: Methods and examples. Social Networks 11(1):1-37, 1989. https://doi.org/10.1016/0378-8733(89)90016-6 """ if not nx.is_connected(G): raise nx.NetworkXError("Graph not connected.") solvername = { "full": FullInverseLaplacian, "lu": SuperLUInverseLaplacian, "cg": CGInverseLaplacian, } n = G.number_of_nodes() ordering = list(reverse_cuthill_mckee_ordering(G)) # make a copy with integer labels according to rcm ordering # this could be done without a copy if we really wanted to H = nx.relabel_nodes(G, dict(zip(ordering, range(n)))) betweenness = dict.fromkeys(H, 0.0) # b[v]=0 for v in H n = H.number_of_nodes() L = laplacian_sparse_matrix( H, nodelist=range(n), weight=weight, dtype=dtype, format="csc" ) C2 = solvername[solver](L, width=1, dtype=dtype) # initialize solver for v in H: col = C2.get_row(v) for w in H: betweenness[v] += col[v] - 2 * col[w] betweenness[w] += col[v] for v in H: betweenness[v] = 1.0 / (betweenness[v]) return {ordering[k]: float(v) for k, v in betweenness.items()} information_centrality = current_flow_closeness_centrality