two version of R2R are here HEAD master

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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/centrality/voterank_alg.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/centrality/voterank_alg.py
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+"""Algorithm to select influential nodes in a graph using VoteRank."""
+
+import networkx as nx
+
+__all__ = ["voterank"]
+
+
+@nx._dispatchable
+def voterank(G, number_of_nodes=None):
+    """Select a list of influential nodes in a graph using VoteRank algorithm
+
+    VoteRank [1]_ computes a ranking of the nodes in a graph G based on a
+    voting scheme. With VoteRank, all nodes vote for each of its in-neighbors
+    and the node with the highest votes is elected iteratively. The voting
+    ability of out-neighbors of elected nodes is decreased in subsequent turns.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    number_of_nodes : integer, optional
+        Number of ranked nodes to extract (default all nodes).
+
+    Returns
+    -------
+    voterank : list
+        Ordered list of computed seeds.
+        Only nodes with positive number of votes are returned.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 4)])
+    >>> nx.voterank(G)
+    [0, 1]
+
+    The algorithm can be used both for undirected and directed graphs.
+    However, the directed version is different in two ways:
+    (i) nodes only vote for their in-neighbors and
+    (ii) only the voting ability of elected node and its out-neighbors are updated:
+
+    >>> G = nx.DiGraph([(0, 1), (2, 1), (2, 3), (3, 4)])
+    >>> nx.voterank(G)
+    [2, 3]
+
+    Notes
+    -----
+    Each edge is treated independently in case of multigraphs.
+
+    References
+    ----------
+    .. [1] Zhang, J.-X. et al. (2016).
+        Identifying a set of influential spreaders in complex networks.
+        Sci. Rep. 6, 27823; doi: 10.1038/srep27823.
+    """
+    influential_nodes = []
+    vote_rank = {}
+    if len(G) == 0:
+        return influential_nodes
+    if number_of_nodes is None or number_of_nodes > len(G):
+        number_of_nodes = len(G)
+    if G.is_directed():
+        # For directed graphs compute average out-degree
+        avgDegree = sum(deg for _, deg in G.out_degree()) / len(G)
+    else:
+        # For undirected graphs compute average degree
+        avgDegree = sum(deg for _, deg in G.degree()) / len(G)
+    # step 1 - initiate all nodes to (0,1) (score, voting ability)
+    for n in G.nodes():
+        vote_rank[n] = [0, 1]
+    # Repeat steps 1b to 4 until num_seeds are elected.
+    for _ in range(number_of_nodes):
+        # step 1b - reset rank
+        for n in G.nodes():
+            vote_rank[n][0] = 0
+        # step 2 - vote
+        for n, nbr in G.edges():
+            # In directed graphs nodes only vote for their in-neighbors
+            vote_rank[n][0] += vote_rank[nbr][1]
+            if not G.is_directed():
+                vote_rank[nbr][0] += vote_rank[n][1]
+        for n in influential_nodes:
+            vote_rank[n][0] = 0
+        # step 3 - select top node
+        n = max(G.nodes, key=lambda x: vote_rank[x][0])
+        if vote_rank[n][0] == 0:
+            return influential_nodes
+        influential_nodes.append(n)
+        # weaken the selected node
+        vote_rank[n] = [0, 0]
+        # step 4 - update voterank properties
+        for _, nbr in G.edges(n):
+            vote_rank[nbr][1] -= 1 / avgDegree
+            vote_rank[nbr][1] = max(vote_rank[nbr][1], 0)
+    return influential_nodes