保留原始权重的加权双峰二部图投影 [英] Weighted Bimodal Bipartite Graph Projection conserving original weights

问题描述

我有一个大的(36k顶点，50k边)加权双峰二部图，我想生成一个投影，不仅像默认的加权实现那样对邻居进行计数，而且还要对边上的权重求和.您可以将其视为包含黑色顶点和蓝色顶点的二部图，在只有蓝色顶点的情况下，我想保留原始图的权重.

I have a large ( 36k vertices, 50k edges ) weighted bimodal bipartite graph and I would like to generate a projection that not only count the neighbors like the default weighted implementation but also sum the weights on the edges. You can think of it as a bipartite graph containing black vertices and blue vertices, where I want to conserve the original graph weights when there are only blue vertices.

我遇到的实现保持橙色值，我对红色表示感兴趣(或者希望得到一个双向加权的投影).

The implementations I came across keep the orange value, I am interested on the red one (or hopefully get a bi-weighted projection).

到目前为止，我已经在igraph，networkx和python-tool中进行了查看，但到目前为止，我只观察到了计算边缘数量的投影.

I've looked so far in igraph, networkx and python-tool but in so far I only observed the projection counting the amount of edges.

Networkx方法generic_weighted_projected_graph(B，节点，weight_function = None)可能可行，但我看不到(sna对我来说是新手，尽管我是Python的用户).

Networkx method generic_weighted_projected_graph(B, nodes, weight_function=None) may make this viable but I can't see how (sna is new to me, although I am an so so python user).

推荐答案

您在它是这样的:

import networkx as nx
from networkx.algorithms import bipartite

edges = [('A1','B1',3),
         ('A1','B2',7),
         ('A2','B1',2),
         ('A2','B2',4),
         ]

B = nx.Graph()
B.add_weighted_edges_from(edges)

def my_weight(G, u, v, weight='weight'):
    w = 0
    for nbr in set(G[u]) & set(G[v]):
        w += G.edge[u][nbr].get(weight, 1) + G.edge[v][nbr].get(weight,1)
    return w

G = bipartite.generic_weighted_projected_graph(B, ['A1', 'A2'], weight_function=my_weight)


print G.edges(data=True)

输出

[('A1', 'A2', {'weight': 16})]

这篇关于保留原始权重的加权双峰二部图投影的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！