diff env/lib/python3.9/site-packages/networkx/algorithms/bipartite/spectral.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.9/site-packages/networkx/algorithms/bipartite/spectral.py	Mon Mar 22 18:12:50 2021 +0000
@@ -0,0 +1,71 @@
+"""
+Spectral bipartivity measure.
+"""
+import networkx as nx
+
+__all__ = ["spectral_bipartivity"]
+
+
+def spectral_bipartivity(G, nodes=None, weight="weight"):
+    """Returns 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)
+    """
+    try:
+        import scipy.linalg
+    except ImportError as e:
+        raise ImportError(
+            "spectral_bipartivity() requires SciPy: ", "http://scipy.org/"
+        ) from e
+    nodelist = list(G)  # ordering of nodes in matrix
+    A = nx.to_numpy_array(G, nodelist, weight=weight)
+    expA = scipy.linalg.expm(A)
+    expmA = scipy.linalg.expm(-A)
+    coshA = 0.5 * (expA + expmA)
+    if nodes is None:
+        # return single number for entire graph
+        return coshA.diagonal().sum() / expA.diagonal().sum()
+    else:
+        # contribution for individual nodes
+        index = dict(zip(nodelist, range(len(nodelist))))
+        sb = {}
+        for n in nodes:
+            i = index[n]
+            sb[n] = coshA[i, i] / expA[i, i]
+        return sb