# Source code for networkx.algorithms.centrality.voterank_alg

```
"""Algorithm to select influential nodes in a graph using VoteRank."""
__all__ = ["voterank"]
[docs]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-neighbours
and the node with the highest votes is elected iteratively. The voting
ability of out-neighbors of elected nodes is decreased in subsequent turns.
Note: We treat each edge independently in case of multigraphs.
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.
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 = []
voterank = {}
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():
voterank[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():
voterank[n][0] = 0
# step 2 - vote
for n, nbr in G.edges():
# In directed graphs nodes only vote for their in-neighbors
voterank[n][0] += voterank[nbr][1]
if not G.is_directed():
voterank[nbr][0] += voterank[n][1]
for n in influential_nodes:
voterank[n][0] = 0
# step 3 - select top node
n = max(G.nodes, key=lambda x: voterank[x][0])
if voterank[n][0] == 0:
return influential_nodes
influential_nodes.append(n)
# weaken the selected node
voterank[n] = [0, 0]
# step 4 - update voterank properties
for _, nbr in G.edges(n):
voterank[nbr][1] -= 1 / avgDegree
voterank[nbr][1] = max(voterank[nbr][1], 0)
return influential_nodes
```