[SOLVED] How to efficiently generate multiple random graphs with random edge weights in networkx


I would like to generate multiple Erdos-Renyi graphs with random edge weights. However, my code works quite slow since there are two nested loops. I was wondering if someone can help me with improving my code.

import networkx as nx
import random

#Suppose I generate 1000 different random graphs
for _ in range(1000):
    #Let's say I will have 100 nodes and the connection probability is 0.4
    G= nx.fast_gnp_random_graph(100,0.4)
    #Then, I assign random edge weights.
    for (u, v) in G.edges():
            G.edges[u,v]['weight'] = random.randint(15,5000)

When I run a similar code block in R using igraph, it is super fast regardless of the size of the network. What are some alternative ways that I can accomplish the same task without facing slow execution time?


This benchmark shows the performance of many graphs libraries (from different languages). It confirms NetworkX is very slow. The graph-tool Python package seems a significantly faster alternative to NetworkX. Please note that the performance of a given package is dependent of what you want to achieve because the performance of a graph algorithm is very dependent of the chosen internal representation.

Answered By – Jérôme Richard

Answer Checked By – Cary Denson (BugsFixing Admin)

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