NetworkX : When I add 'weight' to some node I can not generate adjacency_matrix()?

Question

The moment I add 'weight' to a node, I can no longer generate adjacency_matrix() ? Any ideas of how to still be able to generate it ?

In [73]: g2 = nx.Graph()

In [74]: g2.add_path([1,2,3,5,4,3,1,4,3,7,2])

In [75]: nx.adjacency_matrix(g2)
Out[75]: 
matrix([[ 0.,  1.,  1.,  1.,  0.,  0.],
    [ 1.,  0.,  1.,  0.,  0.,  1.],
    [ 1.,  1.,  0.,  1.,  1.,  1.],
    [ 1.,  0.,  1.,  0.,  1.,  0.],
    [ 0.,  0.,  1.,  1.,  0.,  0.],
    [ 0.,  1.,  1.,  0.,  0.,  0.]])

In [76]: g2[3]['weight'] = 5

In [77]: nx.adjacency_matrix(g2)
---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
 <ipython-input-77-532c786b4588> in <module>()
 ----> 1 nx.adjacency_matrix(g2)

/usr/lib/pymodules/python2.7/networkx/linalg/graphmatrix.pyc in  adjacency_matrix(G, nodelist, weight)
     144     to_dict_of_dicts
     145     """
--> 146     return nx.to_numpy_matrix(G,nodelist=nodelist,weight=weight)
    147 
    148 adj_matrix=adjacency_matrix

/usr/lib/pymodules/python2.7/networkx/convert.pyc in to_numpy_matrix(G, nodelist, dtype, order, multigraph_weight, weight)
    522             for v,d in nbrdict.items():
    523                 try:
--> 524                     M[index[u],index[v]]=d.get(weight,1)
    525                 except KeyError:
    526                     pass

AttributeError: 'int' object has no attribute 'get'

the same goes for :

 In [79]: nx.adjacency_matrix(g2,weight='weight')

Sourin Chatterjee · Accepted Answer · 2023-01-10 05:13:05Z

3

You are close - assign the node weight to g2.nodes[2]['weight'] and it will work.

Note though that the node weights do not appear in the adjacency matrix. It is the edge weights that are assigned there. For example

In [1]: import networkx as nx

In [2]: g2 = nx.Graph()

In [3]: g2.add_path([1,2,3,5,4,3,1,4,3,7,2])

In [4]: g2.nodes[2]['weight']=7

In [5]: g2.nodes
Out[5]: {1: {}, 2: {'weight': 7}, 3: {}, 4: {}, 5: {}, 7: {}}

In [6]: nx.adjacency_matrix(g2).todense()
Out[6]: 
matrix([[0, 1, 1, 1, 0, 0],
        [1, 0, 1, 0, 0, 1],
        [1, 1, 0, 1, 1, 1],
        [1, 0, 1, 0, 1, 0],
        [0, 0, 1, 1, 0, 0],
        [0, 1, 1, 0, 0, 0]])

In [7]: g2.edges[1][2]['weight'] = 42

In [8]: nx.adjacency_matrix(g2).todense()
Out[8]: 
matrix([[ 0, 42,  1,  1,  0,  0],
        [42,  0,  1,  0,  0,  1],
        [ 1,  1,  0,  1,  1,  1],
        [ 1,  0,  1,  0,  1,  0],
        [ 0,  0,  1,  1,  0,  0],
        [ 0,  1,  1,  0,  0,  0]])

Also you will see I am using a newer version of networkx that generates sparse matrices so I have added the .todense() method to get a dense (numpy) matrix. In older versions use "node" and "edge".

edited Jan 10, 2023 at 5:13

Sourin Chatterjee

32 bronze badges

answered Jul 25, 2015 at 20:53

Aric

25.4k6 gold badges83 silver badges77 bronze badges

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1 Comment

FaCoffee Over a year ago

I have a very similar problem, although it involves generating the incidence matrix. I've posted my question here: stackoverflow.com/questions/40130515/…

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