hHSrSSKrS/r\R"SS9SSj5rg)z Laplacian centrality measures. Nlaplacian_centralityweight) edge_attrscSSKnSSKn[U5S:Xa[R"S5eUR US9S:Xa%U(a [ S5eUVs0sHoS_M sn$Ube[URU55n [U 5[U5:wa[R"S5eX Vs/sH oU ;dM UPM sn-n O [U5=p*UR5(a[R"X X4U5n O%[R"X U5R5n URUR R#U SS9S 5R%5n 0n ['U5Hup[UR)U R*S55nUR-U5 U USS24SS2U4nU R/5[1U SS2U45- nUR3UUU5 [U5S:a9URUR R#USS9S 5R%5nOS nU U- nU(aUU - n[5U5X'M U $s snfs snf) a Compute the Laplacian centrality for nodes in the graph `G`. The Laplacian Centrality of a node ``i`` is measured by the drop in the Laplacian Energy after deleting node ``i`` from the graph. The Laplacian Energy is the sum of the squared eigenvalues of a graph's Laplacian matrix. .. math:: C_L(u_i,G) = \frac{(\Delta E)_i}{E_L (G)} = \frac{E_L (G)-E_L (G_i)}{E_L (G)} E_L (G) = \sum_{i=0}^n \lambda_i^2 Where $E_L (G)$ is the Laplacian energy of graph `G`, E_L (G_i) is the Laplacian energy of graph `G` after deleting node ``i`` and $\lambda_i$ are the eigenvalues of `G`'s Laplacian matrix. This formula shows the normalized value. Without normalization, the numerator on the right side is returned. Parameters ---------- G : graph A networkx graph normalized : bool (default = True) If True the centrality score is scaled so the sum over all nodes is 1. If False the centrality score for each node is the drop in Laplacian energy when that node is removed. nodelist : list, optional (default = None) The rows and columns are ordered according to the nodes in nodelist. If nodelist is None, then the ordering is produced by G.nodes(). weight: string or None, optional (default=`weight`) Optional parameter `weight` to compute the Laplacian matrix. The edge data key used to compute each value in the matrix. If None, then each edge has weight 1. walk_type : string or None, optional (default=None) Optional parameter `walk_type` used when calling :func:`directed_laplacian_matrix `. One of ``"random"``, ``"lazy"``, or ``"pagerank"``. If ``walk_type=None`` (the default), then a value is selected according to the properties of `G`: - ``walk_type="random"`` if `G` is strongly connected and aperiodic - ``walk_type="lazy"`` if `G` is strongly connected but not aperiodic - ``walk_type="pagerank"`` for all other cases. alpha : real (default = 0.95) Optional parameter `alpha` used when calling :func:`directed_laplacian_matrix `. (1 - alpha) is the teleportation probability used with pagerank. Returns ------- nodes : dictionary Dictionary of nodes with Laplacian centrality as the value. Examples -------- >>> G = nx.Graph() >>> edges = [(0, 1, 4), (0, 2, 2), (2, 1, 1), (1, 3, 2), (1, 4, 2), (4, 5, 1)] >>> G.add_weighted_edges_from(edges) >>> sorted((v, f"{c:0.2f}") for v, c in laplacian_centrality(G).items()) [(0, '0.70'), (1, '0.90'), (2, '0.28'), (3, '0.22'), (4, '0.26'), (5, '0.04')] Notes ----- The algorithm is implemented based on [1]_ with an extension to directed graphs using the ``directed_laplacian_matrix`` function. Raises ------ NetworkXPointlessConcept If the graph `G` is the null graph. ZeroDivisionError If the graph `G` has no edges (is empty) and normalization is requested. References ---------- .. [1] Qi, X., Fuller, E., Wu, Q., Wu, Y., and Zhang, C.-Q. (2012). Laplacian centrality: A new centrality measure for weighted networks. Information Sciences, 194:240-253. https://math.wvu.edu/~cqzhang/Publication-files/my-paper/INS-2012-Laplacian-W.pdf See Also -------- :func:`~networkx.linalg.laplacianmatrix.directed_laplacian_matrix` :func:`~networkx.linalg.laplacianmatrix.laplacian_matrix` rNz$null graph has no centrality defined)rz(graph with no edges has zero full energyz.nodelist has duplicate nodes or nodes not in GT) eigvals_onlyg)numpyscipylennxNetworkXPointlessConceptsizeZeroDivisionErrorset nbunch_iter NetworkXErrorlist is_directeddirected_laplacian_matrixlaplacian_matrixtoarraypowerlinalgeighsum enumeratearangeshaperemovediagonalabs fill_diagonalfloat)G normalizednodelistr walk_typealphanpspnnodesetnodes lap_matrix full_energylaplace_centralities_dictinode all_but_iA_2new_diag new_energy lapl_cents Z/var/www/html/env/lib/python3.13/site-packages/networkx/algorithms/centrality/laplacian.pyrr s1x 1v{))*PQQvvVv! #$NO O a1a  ammH-. w<3x= (""#ST Tq=q!W,>*4>H!LPPRK!#X&:#3#3A#678 A&q)|4&&(3z!Q$/?+@@ hy12 y>A "))..4."H!LPPRJJ*, !K/I*/ *:!'''* %$Q! >s I%5 I*I*)TNrNgffffff?)__doc__networkxr __all__ _dispatchablerr8r?s; ! "X&NRK%'K%r>