NetworkX: how to add weights to an existing G.edges()?

Question

Given any graph G created in NetworkX, I want to be able to assign some weights to G.edges() after the graph is created. The graphs involved are grids, erdos-reyni, barabasi-albert, and so forth.

Given my G.edges():

[(0, 1), (0, 10), (1, 11), (1, 2), (2, 3), (2, 12), ...]

And my weights:

{(0,1):1.0, (0,10):1.0, (1,2):1.0, (1,11):1.0, (2,3):1.0, (2,12):1.0, ...}

How can I assign each edge the relevant weight? In this trivial case all weights are 1.

I've tried to add the weights to G.edges() directly like this

for i, edge in enumerate(G.edges()):
    G.edges[i]['weight']=weights[edge]

But I get this error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-48-6119dc6b7af0> in <module>()
     10 
     11 for i, edge in enumerate(G.edges()):
---> 12     G.edges[i]['weight']=weights[edge]

TypeError: 'instancemethod' object has no attribute '__getitem__'

What's wrong? Since G.edges() is a list, why can't I access its elements as with any other list?

Solution 1

It fails because edges is a method.

The documentation says to do this like:

G[source][target]['weight'] = weight

For example, the following works for me:

import networkx as nx

G = nx.Graph()

G.add_path([0, 1, 2, 3])

G[0][1]['weight'] = 3

>>> G.get_edge_data(0, 1)
{'weight': 3}

However, your type of code indeed fails:

G.edges[0][1]['weight'] = 3
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-14-97b10ad2279a> in <module>()
----> 1 G.edges[0][1]['weight'] = 3

TypeError: 'instancemethod' object has no attribute '__getitem__'

---

In your case, I'd suggest

for e in G.edges():
    G[e[0]][e[1]] = weights[e]

Solution 2

From the docs:

• You can set all the edge weights at once to the same value with

nx.set_edge_attributes(G, values = 1, name = 'weight')

• Given a dictionary with keys corresponding to edge tuples (your weights), you can assign edge weights to values from that dictionary with

nx.set_edge_attributes(G, values = weights, name = 'weight')

• To view and verify that these edge attributes have been set

G.edges(data = True)

Solution 3

Add edges like this:

g1.add_edge('Mark', 'Edward', weight = 3) g1.add_edge('Joseph', 'Michael', weight = 3) g1.add_edge('Joseph', 'Jason', weight = 4)

And then check whether the graph is weighted:

nx.is_weighted(g1)

True

Categorize weights by their magnitude:

elarge = [(u, v) for (u, v, d) in g1.edges(data=True) if d['weight'] > 4]
esmall = [(u, v) for (u, v, d) in g1.edges(data=True) if d['weight'] <= 4]

Next to display the weighted graph:

pos = nx.spring_layout(g1)  # positions for all nodes

nodes

nx.draw_networkx_nodes(g1, pos, node_size=100)

edges

nx.draw_networkx_edges(g1, pos, edgelist=elarge,
                   width=5)
nx.draw_networkx_edges(g1, pos, edgelist=esmall,
                   width=5, alpha=0.5, edge_color='g', style='dashed')