CODEWITHSUNDEEP
pythonnetwork-programmingnetworkx
niuyw
niuyw

Reputation: 29

How to generate a random network but keep the original node degree using networkx?

I have a network, and how to generate a random network but ensure each node retains the same degre of the original network using networkx? My first thought is to get the adjacency matrix, and perform a random in each row of the matrix, but this way is somwhat complex, e.g. need to avoid self-conneted (which is not seen in the original network) and re-label the nodes. Thanks!

Upvotes: 1

Views: 2641

Answers (1)

jtimmons
jtimmons

Reputation: 386

I believe what you're looking for is expected_degree_graph. It generates a random graph based on a sequence of expected degrees, where each degree in the list corresponds to a node. It also even includes an option to disallow self-loops!

You can get a list of degrees using networkx.degree. Here's an example of how you would use them together in networkx 2.0+ (degree is slightly different in 1.0):

import networkx as nx
from networkx.generators.degree_seq import expected_degree_graph

N,P = 3, 0.5
G = nx.generators.random_graphs.gnp_random_graph(N, P)
G2 = expected_degree_graph([deg for (_, deg) in G.degree()], selfloops=False)

Note that you're not guaranteed to have the exact degrees for each node using expected_degree_graph; as the name implies, it's probabilistic given the expected value for each of the degrees. If you want something a little more concrete you can use configuration_model, however it does not protect against parallel edges or self-loops, so you'd need to prune those out and replace the edges yourself.

Upvotes: 2

Related Questions