Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/networkx/algorithms/non_randomness.py

@@ -0,0 +1,82 @@
+r""" Computation of graph non-randomness
+"""
+
+import math
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["non_randomness"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def non_randomness(G, k=None):
+    """Compute the non-randomness of graph G.
+
+    The first returned value nr is the sum of non-randomness values of all
+    edges within the graph (where the non-randomness of an edge tends to be
+    small when the two nodes linked by that edge are from two different
+    communities).
+
+    The second computed value nr_rd is a relative measure that indicates
+    to what extent graph G is different from random graphs in terms
+    of probability. When it is close to 0, the graph tends to be more
+    likely generated by an Erdos Renyi model.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Graph must be binary, symmetric, connected, and without self-loops.
+
+    k : int
+        The number of communities in G.
+        If k is not set, the function will use a default community
+        detection algorithm to set it.
+
+    Returns
+    -------
+    non-randomness : (float, float) tuple
+        Non-randomness, Relative non-randomness w.r.t.
+        Erdos Renyi random graphs.
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> nr, nr_rd = nx.non_randomness(G, 2)
+
+    Notes
+    -----
+    This computes Eq. (4.4) and (4.5) in Ref. [1]_.
+
+    References
+    ----------
+     .. [1] Xiaowei Ying and Xintao Wu,
+            On Randomness Measures for Social Networks,
+            SIAM International Conference on Data Mining. 2009
+    """
+
+    if not nx.is_connected(G):
+        raise nx.NetworkXException("Non connected graph.")
+    if len(list(nx.selfloop_edges(G))) > 0:
+        raise nx.NetworkXError("Graph must not contain self-loops")
+
+    if k is None:
+        k = len(tuple(nx.community.label_propagation_communities(G)))
+
+    try:
+        import numpy as np
+    except ImportError as e:
+        msg = "non_randomness requires NumPy: http://numpy.org/"
+        raise ImportError(msg) from e
+
+    # eq. 4.4
+    nr = np.real(np.sum(np.linalg.eigvals(nx.to_numpy_array(G))[:k]))
+
+    n = G.number_of_nodes()
+    m = G.number_of_edges()
+    p = (2 * k * m) / (n * (n - k))
+
+    # eq. 4.5
+    nr_rd = (nr - ((n - 2 * k) * p + k)) / math.sqrt(2 * k * p * (1 - p))
+
+    return nr, nr_rd