two version of R2R are hereHEADmaster

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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/non_randomness.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/non_randomness.py
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+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")
+@nx._dispatchable(edge_attrs="weight")
+def non_randomness(G, k=None, weight="weight"):
+    """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 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.
+
+    weight : string or None, optional (default=None)
+        The name of an edge attribute that holds the numerical value used
+        as a weight. If None, then each edge has weight 1, i.e., the graph is
+        binary.
+
+    Returns
+    -------
+    non-randomness : (float, float) tuple
+        Non-randomness, Relative non-randomness w.r.t.
+        Erdos Renyi random graphs.
+
+    Raises
+    ------
+    NetworkXException
+        if the input graph is not connected.
+    NetworkXError
+        if the input graph contains self-loops or if graph has no edges.
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> nr, nr_rd = nx.non_randomness(G, 2)
+    >>> nr, nr_rd = nx.non_randomness(G, 2, "weight")
+
+    Notes
+    -----
+    This computes Eq. (4.4) and (4.5) in Ref. [1]_.
+
+    If a weight field is passed, this algorithm will use the eigenvalues
+    of the weighted adjacency matrix to compute Eq. (4.4) and (4.5).
+
+    References
+    ----------
+    .. [1] Xiaowei Ying and Xintao Wu,
+           On Randomness Measures for Social Networks,
+           SIAM International Conference on Data Mining. 2009
+    """
+    import numpy as np
+
+    # corner case: graph has no edges
+    if nx.is_empty(G):
+        raise nx.NetworkXError("non_randomness not applicable to empty graphs")
+    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)))
+
+    # eq. 4.4
+    eigenvalues = np.linalg.eigvals(nx.to_numpy_array(G, weight=weight))
+    nr = float(np.real(np.sum(eigenvalues[: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