degree_centrality() got an unexpected keyword argument 'weight'是什么错误
时间: 2023-11-19 08:04:39 浏览: 44
这个错误通常是由于传递了无效的参数导致的。在 NetworkX 中,`degree_centrality()` 函数没有 `weight` 参数,所以如果你传递了 `weight` 参数,就会导致该错误。
可能的解决方案是检查你的代码,确保你正在使用正确的参数。如果你想要考虑节点权重,可以使用 `weighted_degree_centrality()` 函数。
相关问题
degree_centrality() got an unexpected keyword argument 'weight'
The error message "degree_centrality() got an unexpected keyword argument 'weight'" indicates that you are trying to use the 'weight' parameter with the degree_centrality() function, which does not support it. The 'weight' parameter is typically used with other network analysis functions, such as betweenness_centrality() or eigenvector_centrality().
In order to calculate degree centrality with weighted edges, you can use the weighted version of the degree_centrality() function, which is available in the NetworkX library. You can import it with the following code:
```python
from networkx.algorithms.centrality import degree_centrality
G = nx.Graph()
# add nodes and weighted edges to the graph
# ...
# calculate degree centrality with weighted edges
centrality = degree_centrality(G, weight='weight')
```
Make sure you have the latest version of the NetworkX library installed, as this feature may not be available in older versions.
TypeError: closeness_centrality() got an unexpected keyword argument 'reverse'
如果你使用的是 NetworkX 2.4 版本或更早的版本,则 `closeness_centrality()` 函数不支持 `reverse` 参数,因为该参数是在 NetworkX 2.5 版本中添加的。此时,可以使用 `nx.reverse()` 函数将有向图反转后再计算接近中心性。下面是 Python 代码实现有向网络接近中心性的例子(适用于 NetworkX 2.4 版本或更早版本):
```python
import networkx as nx
# 创建一个简单的有向图
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)])
# 计算入度接近中心性
in_closeness_centrality = nx.closeness_centrality(nx.reverse(G), wf_improved=False)
# 计算出度接近中心性
out_closeness_centrality = nx.closeness_centrality(G, wf_improved=False)
# 输出每个节点的入度和出度接近中心性
for node, in_centrality in in_closeness_centrality.items():
out_centrality = out_closeness_centrality[node]
print(f"Node {node}: In-Closeness Centrality = {in_centrality}, Out-Closeness Centrality = {out_centrality}")
```
在上面的代码中,我们使用 NetworkX 库创建了一个简单的有向图,并使用 `nx.reverse()` 函数将图反转后计算入度接近中心性。需要注意的是,我们分别对图的入度和出度计算接近中心性,因此需要分别计算反转后的图和原图的接近中心性。最后,我们遍历每个节点,输出它的入度和出度接近中心性。
需要注意的是,在计算接近中心性时,我们同样使用了 `nx.closeness_centrality()` 函数。该函数分别计算了节点到其他节点的最短入度路径和最短出度路径长度,因此需要保证图联通。如果图不联通,则可能会导致函数返回错误的结果。