fi+%rdZddlZddlmZgdZededejdZededejdZededejd Z ejd Z dS) z5 Subraph centrality and communicability betweenness. N)not_implemented_for)subgraph_centrality_expsubgraph_centrality&communicability_betweenness_centrality estrada_indexdirected multigraphc  ddl}t|}tj||}d||dk<|j|}t t|tt| }|S)aReturns the subgraph centrality for each node of G. Subgraph centrality of a node `n` is the sum of weighted closed walks of all lengths starting and ending at node `n`. The weights decrease with path length. Each closed walk is associated with a connected subgraph ([1]_). Parameters ---------- G: graph Returns ------- nodes:dictionary Dictionary of nodes with subgraph centrality as the value. Raises ------ NetworkXError If the graph is not undirected and simple. See Also -------- subgraph_centrality: Alternative algorithm of the subgraph centrality for each node of G. Notes ----- This version of the algorithm exponentiates the adjacency matrix. The subgraph centrality of a node `u` in G can be found using the matrix exponential of the adjacency matrix of G [1]_, .. math:: SC(u)=(e^A)_{uu} . References ---------- .. [1] Ernesto Estrada, Juan A. Rodriguez-Velazquez, "Subgraph centrality in complex networks", Physical Review E 71, 056103 (2005). https://arxiv.org/abs/cond-mat/0504730 Examples -------- (Example from [1]_) >>> G = nx.Graph( ... [ ... (1, 2), ... (1, 5), ... (1, 8), ... (2, 3), ... (2, 8), ... (3, 4), ... (3, 6), ... (4, 5), ... (4, 7), ... (5, 6), ... (6, 7), ... (7, 8), ... ] ... ) >>> sc = nx.subgraph_centrality_exp(G) >>> print([f"{node} {sc[node]:0.2f}" for node in sorted(sc)]) ['1 3.90', '2 3.90', '3 3.64', '4 3.71', '5 3.64', '6 3.71', '7 3.64', '8 3.90'] rNg) scipylistnxto_numpy_arraylinalgexpmdictzipmapfloatdiagonal)GspnodelistAexpAscs /var/www/development/aibuddy-work/election-extract/venv/lib/python3.11/site-packages/networkx/algorithms/centrality/subgraph_alg.pyrrs|RAwwH !X&&AAa3hK 9>>!  D c(Ct}}7788 9 9B Ic ddl}t|}tj||}d|||<|j|\}}||dz}||}||z}tt|tt|} | S)aReturns subgraph centrality for each node in G. Subgraph centrality of a node `n` is the sum of weighted closed walks of all lengths starting and ending at node `n`. The weights decrease with path length. Each closed walk is associated with a connected subgraph ([1]_). Parameters ---------- G: graph Returns ------- nodes : dictionary Dictionary of nodes with subgraph centrality as the value. Raises ------ NetworkXError If the graph is not undirected and simple. See Also -------- subgraph_centrality_exp: Alternative algorithm of the subgraph centrality for each node of G. Notes ----- This version of the algorithm computes eigenvalues and eigenvectors of the adjacency matrix. Subgraph centrality of a node `u` in G can be found using a spectral decomposition of the adjacency matrix [1]_, .. math:: SC(u)=\sum_{j=1}^{N}(v_{j}^{u})^2 e^{\lambda_{j}}, where `v_j` is an eigenvector of the adjacency matrix `A` of G corresponding to the eigenvalue `\lambda_j`. Examples -------- (Example from [1]_) >>> G = nx.Graph( ... [ ... (1, 2), ... (1, 5), ... (1, 8), ... (2, 3), ... (2, 8), ... (3, 4), ... (3, 6), ... (4, 5), ... (4, 7), ... (5, 6), ... (6, 7), ... (7, 8), ... ] ... ) >>> sc = nx.subgraph_centrality(G) >>> print([f"{node} {sc[node]:0.2f}" for node in sorted(sc)]) ['1 3.90', '2 3.90', '3 3.64', '4 3.71', '5 3.64', '6 3.71', '7 3.64', '8 3.90'] References ---------- .. [1] Ernesto Estrada, Juan A. Rodriguez-Velazquez, "Subgraph centrality in complex networks", Physical Review E 71, 056103 (2005). https://arxiv.org/abs/cond-mat/0504730 rNr ) numpyr rrnonzeroreigharrayexprrrr) rnprrwvvsquareexpwxgrs rrresZAwwH !X&&AAbjjmm 9>>!  DAqhhqkkQG 66!99D 4B c(CrNN++ , ,B Ircddl}ddl}t|}t|}t j||}d|||<|j|}tt|t|}i}|D]} || } || ddf } |dd| f } d|| ddf<d|dd| f<||j|z |z } d| | ddf<d| dd| f<| | | | z} t| || <| || ddf<| |dd| f<t|}|dkr1d|dz dz|dz z z fd|D}|S)aReturns subgraph communicability for all pairs of nodes in G. Communicability betweenness measure makes use of the number of walks connecting every pair of nodes as the basis of a betweenness centrality measure. Parameters ---------- G: graph Returns ------- nodes : dictionary Dictionary of nodes with communicability betweenness as the value. Raises ------ NetworkXError If the graph is not undirected and simple. Notes ----- Let `G=(V,E)` be a simple undirected graph with `n` nodes and `m` edges, and `A` denote the adjacency matrix of `G`. Let `G(r)=(V,E(r))` be the graph resulting from removing all edges connected to node `r` but not the node itself. The adjacency matrix for `G(r)` is `A+E(r)`, where `E(r)` has nonzeros only in row and column `r`. The subraph betweenness of a node `r` is [1]_ .. math:: \omega_{r} = \frac{1}{C}\sum_{p}\sum_{q}\frac{G_{prq}}{G_{pq}}, p\neq q, q\neq r, where `G_{prq}=(e^{A}_{pq} - (e^{A+E(r)})_{pq}` is the number of walks involving node r, `G_{pq}=(e^{A})_{pq}` is the number of closed walks starting at node `p` and ending at node `q`, and `C=(n-1)^{2}-(n-1)` is a normalization factor equal to the number of terms in the sum. The resulting `\omega_{r}` takes values between zero and one. The lower bound cannot be attained for a connected graph, and the upper bound is attained in the star graph. References ---------- .. [1] Ernesto Estrada, Desmond J. Higham, Naomichi Hatano, "Communicability Betweenness in Complex Networks" Physica A 388 (2009) 764-774. https://arxiv.org/abs/0905.4102 Examples -------- >>> G = nx.Graph([(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)]) >>> cbc = nx.communicability_betweenness_centrality(G) >>> print([f"{node} {cbc[node]:0.2f}" for node in sorted(cbc)]) ['0 0.03', '1 0.45', '2 0.51', '3 0.45', '4 0.40', '5 0.19', '6 0.03'] rNr r g?c"i|] \}}||z Sr.).0nodevaluescales r z:communicability_betweenness_centrality..$s#BBB{tUtUU]BBBr)r!r r lenrrr"rrrrrangecopydiagrsumitems)rr&rrnrrmappingcbcr(irowcolBorderr2s @rrrsHAwwH H A !X&&AAbjjmm 9>>!  D3xq**++G C  AJ111gllnn1gllnn!QQQ$!!!Q$ BINN1%% % -!QQQ$!!!Q$ RWWRWWQZZ  quuwwA!QQQ$!!!Q$ HHE qyy )US[9:BBBBciikkBBB Jrc^tt|S)uReturns the Estrada index of a the graph G. The Estrada Index is a topological index of folding or 3D "compactness" ([1]_). Parameters ---------- G: graph Returns ------- estrada index: float Raises ------ NetworkXError If the graph is not undirected and simple. Notes ----- Let `G=(V,E)` be a simple undirected graph with `n` nodes and let `\lambda_{1}\leq\lambda_{2}\leq\cdots\lambda_{n}` be a non-increasing ordering of the eigenvalues of its adjacency matrix `A`. The Estrada index is ([1]_, [2]_) .. math:: EE(G)=\sum_{j=1}^n e^{\lambda _j}. References ---------- .. [1] E. Estrada, "Characterization of 3D molecular structure", Chem. Phys. Lett. 319, 713 (2000). https://doi.org/10.1016/S0009-2614(00)00158-5 .. [2] José Antonio de la Peñaa, Ivan Gutman, Juan Rada, "Estimating the Estrada index", Linear Algebra and its Applications. 427, 1 (2007). https://doi.org/10.1016/j.laa.2007.06.020 Examples -------- >>> G = nx.Graph([(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)]) >>> ei = nx.estrada_index(G) >>> print(f"{ei:0.5}") 20.55 )r8rvalues)rs rrr(s(\ "1%%,,.. / //r) __doc__networkxrnetworkx.utilsr__all__ _dispatchablerrrrr.rrrIsV......   Z  \""OO#"! OdZ  \""VV#"! VrZ  \""aa#"! aH-0-0-0-0-0r