two version of R2R are here HEAD master

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diff --git a/.venv/lib/python3.12/site-packages/networkx/generators/spectral_graph_forge.py b/.venv/lib/python3.12/site-packages/networkx/generators/spectral_graph_forge.py
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+"""Generates graphs with a given eigenvector structure"""
+
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = ["spectral_graph_forge"]
+
+
+@np_random_state(3)
+@nx._dispatchable(returns_graph=True)
+def spectral_graph_forge(G, alpha, transformation="identity", seed=None):
+    """Returns a random simple graph with spectrum resembling that of `G`
+
+    This algorithm, called Spectral Graph Forge (SGF), computes the
+    eigenvectors of a given graph adjacency matrix, filters them and
+    builds a random graph with a similar eigenstructure.
+    SGF has been proved to be particularly useful for synthesizing
+    realistic social networks and it can also be used to anonymize
+    graph sensitive data.
+
+    Parameters
+    ----------
+    G : Graph
+    alpha :  float
+        Ratio representing the percentage of eigenvectors of G to consider,
+        values in [0,1].
+    transformation : string, optional
+        Represents the intended matrix linear transformation, possible values
+        are 'identity' and 'modularity'
+    seed : integer, random_state, or None (default)
+        Indicator of numpy random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    H : Graph
+        A graph with a similar eigenvector structure of the input one.
+
+    Raises
+    ------
+    NetworkXError
+        If transformation has a value different from 'identity' or 'modularity'
+
+    Notes
+    -----
+    Spectral Graph Forge (SGF) generates a random simple graph resembling the
+    global properties of the given one.
+    It leverages the low-rank approximation of the associated adjacency matrix
+    driven by the *alpha* precision parameter.
+    SGF preserves the number of nodes of the input graph and their ordering.
+    This way, nodes of output graphs resemble the properties of the input one
+    and attributes can be directly mapped.
+
+    It considers the graph adjacency matrices which can optionally be
+    transformed to other symmetric real matrices (currently transformation
+    options include *identity* and *modularity*).
+    The *modularity* transformation, in the sense of Newman's modularity matrix
+    allows the focusing on community structure related properties of the graph.
+
+    SGF applies a low-rank approximation whose fixed rank is computed from the
+    ratio *alpha* of the input graph adjacency matrix dimension.
+    This step performs a filtering on the input eigenvectors similar to the low
+    pass filtering common in telecommunications.
+
+    The filtered values (after truncation) are used as input to a Bernoulli
+    sampling for constructing a random adjacency matrix.
+
+    References
+    ----------
+    ..  [1] L. Baldesi, C. T. Butts, A. Markopoulou, "Spectral Graph Forge:
+        Graph Generation Targeting Modularity", IEEE Infocom, '18.
+        https://arxiv.org/abs/1801.01715
+    ..  [2] M. Newman, "Networks: an introduction", Oxford university press,
+        2010
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> H = nx.spectral_graph_forge(G, 0.3)
+    >>>
+    """
+    import numpy as np
+    import scipy as sp
+
+    available_transformations = ["identity", "modularity"]
+    alpha = np.clip(alpha, 0, 1)
+    A = nx.to_numpy_array(G)
+    n = A.shape[1]
+    level = round(n * alpha)
+
+    if transformation not in available_transformations:
+        msg = f"{transformation!r} is not a valid transformation. "
+        msg += f"Transformations: {available_transformations}"
+        raise nx.NetworkXError(msg)
+
+    K = np.ones((1, n)) @ A
+
+    B = A
+    if transformation == "modularity":
+        B -= K.T @ K / K.sum()
+
+    # Compute low-rank approximation of B
+    evals, evecs = np.linalg.eigh(B)
+    k = np.argsort(np.abs(evals))[::-1]  # indices of evals in descending order
+    evecs[:, k[np.arange(level, n)]] = 0  # set smallest eigenvectors to 0
+    B = evecs @ np.diag(evals) @ evecs.T
+
+    if transformation == "modularity":
+        B += K.T @ K / K.sum()
+
+    B = np.clip(B, 0, 1)
+    np.fill_diagonal(B, 0)
+
+    for i in range(n - 1):
+        B[i, i + 1 :] = sp.stats.bernoulli.rvs(B[i, i + 1 :], random_state=seed)
+        B[i + 1 :, i] = np.transpose(B[i, i + 1 :])
+
+    H = nx.from_numpy_array(B)
+
+    return H