CODEWITHSUNDEEP
pythonnetworkx
LascieL
LascieL

Reputation: 47

Extracting graphs with 2 nodes using networkx and python

I have a networkx graph with many nodes and edges. Some of the nodes share only one edge with another node. How do I extract a list of the isolated 2 node graphs?

The table of data I am working with looks like this

>|Name1|Name2|distance|
>|----|----|----|
>|AAA|BBB|2.315544|
>|AAB|BBB|2.576293|
>|AAC|BBB|2.967239|
>|AAD|BBB|2.779942|
>|...|...|...|

The graph looks like this:

enter image description here

Alternatively, is there a way to generate a list of clusters?

Upvotes: 0

Views: 280

Answers (1)

Paul Brodersen
Paul Brodersen

Reputation: 13021

Your "clusters" are typically called components in network analysis. In networkx, you can compute them using the function nx.connected_components, which returns an iterator over components. Each component returned by the iterator simply a list of nodes.

import networkx as nx

g = nx.Graph()
... # add nodes & edges 
components = nx.connected_components(g)
for component in components:
    if len(components) == 2:
        # do something special with components of size 2

If you are only interested in the largest component (often called "giant component"), it is worth noting that you can then sort the components or find the largest group by post-processing this output as detailed in the documentation:

Generate a sorted list of connected components, largest first.

>>> G = nx.path_graph(4)
>>> nx.add_path(G, [10, 11, 12])
>>> [len(c) for c in sorted(nx.connected_components(G), key=len, reverse=True)]
[4, 3]

If you only want the largest connected component, it's more
efficient to use max instead of sort.

>>> largest_cc = max(nx.connected_components(G), key=len)

Upvotes: 1

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