## Mercurial > repos > shellac > sam_consensus_v3

### view env/lib/python3.9/site-packages/networkx/algorithms/centrality/voterank_alg.py @ 0:4f3585e2f14b draft default tip

Find changesets by keywords (author, files, the commit message), revision
number or hash, or revset expression.

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

author | shellac |
---|---|

date | Mon, 22 Mar 2021 18:12:50 +0000 |

parents | |

children |

line wrap: on

line source

"""Algorithm to select influential nodes in a graph using VoteRank.""" __all__ = ["voterank"] 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