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Ailurophile
Ailurophile

Reputation: 3005

How to generate the component id in the Networkx graph?

I have a large Graph Network generated using Networkx package.

enter image description here

Here I'm adding a sample

import networkx as nx
import pandas as pd

G = nx.path_graph(4)
nx.add_path(G, [10, 11, 12])

I'm trying to create a dataframe with Node, degrees, component id, component.

Created degrees using

degrees = list(nx.degree(G))

data = pd.DataFrame([list(d) for d in degrees], columns=['Node', 'degree']).sort_values('degree', ascending=False)

extracted components using

Gcc = sorted(nx.connected_components(G), key=len, reverse=True)

Gcc

[{0, 1, 2, 3}, {10, 11, 12}]

And not sure how I can create the Component ID and components in the data.

Required output:

  Node  degree  ComponentID  Components
1   1   2           1         {0, 1, 2, 3}
2   2   2           1         {0, 1, 2, 3}
5   11  2           2         {10, 11, 12}
0   0   1           1         {0, 1, 2, 3}
3   3   1           1         {0, 1, 2, 3}
4   10  1           2         {10, 11, 12}
6   12  1           2         {10, 11, 12}

How to generate the component ids and add them to the nodes and degrees?

Upvotes: 3

Views: 641

Answers (1)

Shubham Sharma
Shubham Sharma

Reputation: 71707

Create triplets of Node, ComponentId and Component by enumerating over the connected component list, then create a new dataframe from these triplets and merge it with the given dataframe on Node

df = pd.DataFrame([(n, i, c) for i,c in enumerate(Gcc, 1) for n in c], 
                        columns=['Node', 'ComponentID', 'Components'])

data = data.merge(df, on='Node')

Alternatively you can use map instead of merge to individually create ComponentID and Components columns

d = dict(enumerate(Gcc, 1))
data['ComponentID'] = data['Node'].map({n:i for i,c in d.items() for n in c})
data['Components']  = data['ComponentID'].map(d)

print(data)

   Node  degree  ComponentID    Components
1     1       2            1  {0, 1, 2, 3}
2     2       2            1  {0, 1, 2, 3}
5    11       2            2  {10, 11, 12}
0     0       1            1  {0, 1, 2, 3}
3     3       1            1  {0, 1, 2, 3}
4    10       1            2  {10, 11, 12}
6    12       1            2  {10, 11, 12}

Upvotes: 3

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