CODEWITHSUNDEEP
pythonnetworkx
Haliaetus
Haliaetus

Reputation: 490

How to pass edge attribute as the edge distance to nx.closeness_centrality()?

Suppose I have a graph defined by this matrix:

test = np.array([[0, 0, 4, 0],
                 [0, 0, 6, 0],
                 [4, 6, 0, 10],
                 [0, 0, 10, 0]])

import networkx as nx

test_nx = nx.from_numpy_array(test)

Next, I'd like to compute the weighted closeness centrality for each node of this graph.

nx.closeness_centrality(test_nx, distance="edges")

I get:

{0: 0.6, 1: 0.6, 2: 1.0, 3: 0.6}

However, this is clearly not considering edge weights. I'm guessing the reason is I'm not passing the "distance" argument properly.

According to the docs:

closeness_centrality(G, u=None, distance=None, normalized=True)

distance (edge attribute key, optional (default=None)) – Use the
specified edge attribute as the edge distance in shortest path
calculations

Can anyone advise me how to pass edge weights to this function? My desired output would be a dictionary of closeness centrality values (one per node) which considers that these edges have weights and they are not simply binary.

Upvotes: 2

Views: 506

Answers (1)

Michal Yanko
Michal Yanko

Reputation: 389

If you look at the edges using this:

print(test_nx.edges(data=True))
# output: [(0, 2, {'weight': 4}), (1, 2, {'weight': 6}), (2, 3, {'weight': 10})]

you can see that the key used to save the edge weight is weight. The right distance key will be this one.

nx.closeness_centrality(test_nx, distance="weight")
# output {0: 0.10714285714285714, 1: 0.09375, 2: 0.15, 3: 0.075}

Upvotes: 1

Related Questions