+ jnNRt^RItR.t]P!RR7RRl4tR#)z Spectral bipartivity measure. Nspectral_bipartivityweight) edge_attrsc B^RIp\V4p\P!WVR7pVPP V4pVPP V)4pRWg,,pVfK\ VP4P4VP4P4, 4#\\V\\V4444p /p VF4p W,p VPW4VPW4, W&K6 V #)uReturns the spectral bipartivity. Parameters ---------- G : NetworkX graph nodes : list or container optional(default is all nodes) Nodes to return value of spectral bipartivity contribution. weight : string or None optional (default = 'weight') Edge data key to use for edge weights. If None, weights set to 1. Returns ------- sb : float or dict A single number if the keyword nodes is not specified, or a dictionary keyed by node with the spectral bipartivity contribution of that node as the value. Examples -------- >>> from networkx.algorithms import bipartite >>> G = nx.path_graph(4) >>> bipartite.spectral_bipartivity(G) 1.0 Notes ----- This implementation uses Numpy (dense) matrices which are not efficient for storing large sparse graphs. See Also -------- color References ---------- .. [1] E. Estrada and J. A. Rodríguez-Velázquez, "Spectral measures of bipartivity in complex networks", PhysRev E 72, 046105 (2005) N)rg?)scipylistnxto_numpy_arraylinalgexpmfloatdiagonalsumdictziprangelenitem) GnodesrspnodelistAexpAexpmAcoshAindexsbnis &&& g/home/luca/screen-checking/.venv/lib/python3.14/site-packages/networkx/algorithms/bipartite/spectral.pyrr sTAwH !f5A 99>>! D IINNA2 E 4< E }U^^%))+dmmo.A.A.CCDDS5X#789 AAJJq$tyy6BE )Nr)__doc__networkxr__all__ _dispatchablerr!r r's6 ! "X&:':r!