在Gephi中打开之前，在Networkx write_graphml中添加属性

Question

I have a dataframe consisting of possible network connections in the format df = pd.DataFrame(["A", "B", "Count", "some_attribute"]) . This dataframe represents connections like this:

• A has a connection with B
• This connection occurred "Count" times
• This connection has a specific attribute (ie a specific type of contact)

I want to export this Dataframe to the graphml format. It works fine using the following code:

import networkx as nx
G = nx.Graph()
G.add_weighted_edges_from(df[["A", "B", "Count"]].values)
nx.write_graphml(G, "my_graph.graphml")

This code results in a graphml file with the correct graph, which I can use with Gephi. Now I want to add an attribute:

G = nx.Graph()
G.add_weighted_edges_from(df[["A", "B", "Count"]].values, attr=df["some_attribute"].values)
nx.write_graphml(G, "my_graph.graphml")

Whenever I try to add attributes in this code, it becomes impossible to write it to a graphml file. With this code, I get the following error message:

NetworkXError: GraphML writer does not support <class 'numpy.ndarray'> as data values.

I found related articles (like this one), but it didn't provide any solution for this problem. Does anyone have a solution for adding attributes to a graphml file using networkx so I can use them in Gephi?

Answer 1

Assuming the random DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [0,1,2,0,0],
                   'B': [1,2,3,2,3],
                   'Count': [1,2,5,1,1],
                   'some_attribute': ['red','blue','red','blue','red']})

    A   B   Count  some_attribute
0   0   1   1   red
1   1   2   2   blue
2   2   3   5   red
3   0   2   1   blue
4   0   3   1   red

Following the code from above to instantiate a Graph :

import networkx as nx    
G = nx.Graph()
G.add_weighted_edges_from(df[["A","B", "Count"]].values, attr=df["some_attribute"].values)

when inspecting an edge, it appears that the numpy array, df['some_attribute'].values , gets assigned as an attribute to each edge:

print (G.edge[0][1])
print (G.edge[2][3])
{'attr': array(['red', 'blue', 'red', 'blue', 'red'], dtype=object), 'weight': 1}
{'attr': array(['red', 'blue', 'red', 'blue', 'red'], dtype=object), 'weight': 5}

If I understand your intent correctly, I'm assuming you want each edge's attribute to correspond to the df['some_attribute'] column.

You may find it easier to create your Graph using nx.from_pandas_dataframe() , especially since you already have data formatted in a DataFrame object.

G = nx.from_pandas_dataframe(df, 'A', 'B', ['Count', 'some_attribute'])

print (G.edge[0][1])
print (G.edge[2][3])
{'Count': 1, 'some_attribute': 'red'}
{'Count': 5, 'some_attribute': 'red'}

writing to file was no problem:

nx.write_graphml(G,"my_graph.graphml")

except, I'm not a regular Gephi user so there may be another way to solve the following. When I loaded the file with 'Count' as the edge attribute, the Gephi graph didn't recognize edge weights by default. So I changed the column name from 'Count' to 'weight' and saw the following when I loaded into Gephi:

df.columns=['A', 'B', 'weight', 'some_attribute']
G = nx.from_pandas_dataframe(df, 'A', 'B', ['weight', 'some_attribute'])
nx.write_graphml(G,"my_graph.graphml")

Hope this helps and that I understood your question correctly.

Edit

Per Corley's comment above, you can use the following if you choose to use add_edges_from .

G.add_edges_from([(u,v,{'weight': w, 'attr': a}) for u,v,w,a in df[['A', 'B', 'Count', 'some_attribute']].values ])

There is no significant performance gain, however I find from_pandas_dataframe more readable.

import numpy as np

df = pd.DataFrame({'A': np.arange(0,1000000),
                   'B': np.arange(1,1000001),
                   'Count': np.random.choice(range(10), 1000000, replace=True),
                   'some_attribute': np.random.choice(['red','blue'], 1000000, replace=True,)})

%%timeit
G = nx.Graph()
G.add_edges_from([(u,v,{'weight': w, 'attr': a}) for u,v,w,a in df[['A', 'B', 'Count', 'some_attribute']].values ])

1 loop, best of 3: 4.23 s per loop

%%timeit
G = nx.Graph()
G = nx.from_pandas_dataframe(df, 'A', 'B', ['Count', 'some_attribute'])

1 loop, best of 3: 3.93 s per loop