<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting an intricate cryptographic protocol that simulates secure communications over a network, and you want to test its resilience on different network topologies. You're currently focusing on a network represented as a complete graph of 5 nodes, which is akin to a scenario where every participant can directly communicate with every other. For the purpose of this simulation, you require a method to generate a new network topology that maintains the original's connectedness but alters its structure.

Would you be able to employ the NetworkX library's `random_reference` function to iteratively randomize the network by strategically swapping edges, ensuring that the full connectivity of the network remains intact? This procedure would provide you with a distinct graph that still adheres to the fundamental requirement of allowing secure paths for communication between any pair of nodes, thereby keeping the network robust for the simulation of your cryptographic protocols.
### Response:
import networkx as nx

# Create a graph
G = nx.complete_graph(5)

# Compute a random graph by swapping edges of the original graph
randomized_graph = nx.random_reference(G, niter=5, seed=42)

print('Edges of the original graph:', G.edges())
print('Edges of the randomized graph:', randomized_graph.edges())
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