'Fast way to generate 1000 random graphs given a Networkx graph
I need to generate multiple (~1000 random graphs) from a given node degree and node list. I found that the fastest way is to use networkx configuration model. Although it takes a long time to generate 1 graph in my case (nodes = 20,000; edges = 350 million). So instead of using a configuration model 1000 times to build a random graph from scratch. I thought of using the 1 graph I generated and using an edge swap or randomization algorithm on it to generate more graphs. With motivation being speed. But I am not sure how to generate random graphs given an initial graph while maintaining the node degree?
G_2 = nx.configuration_model(deg_sequence= grid_ids_withlinks.values())
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