<｜begin▁of▁sentence｜>### Instruction:
In today's segment on the wonders of computer science and graph theory, we delve into a fascinating scenario: imagine we have two intricate networks, each representing a unique system of connections—much like the social networks or transportation grids we often report on. These networks, which we'll call Network A and Network B for simplicity, appear to have the same pattern of connections—they're both formed by linking points 0, 1, and 2 in a triangular fashion.

Now, the intriguing question posed by experts in the field is whether it's feasible, with the help of a computational method known as 'fast_could_be_isomorphic,' to assess the possibility that these two networks, A and B, are actually mirror images of one another—in other words, whether they are isomorphic.

This is not just an abstract exercise; determining the isomorphism of graphs can have real implications for understanding the underlying structure of data networks, biochemical pathways, or even organizational hierarchies. So, can this high-speed algorithm give us an indication that Network A and Network B might be two different representations of the same network? That's the question tech analysts are buzzing about—and we're eager to understand the potential connections.
### Response:
import networkx as nx

# Create two graphs
G1 = nx.Graph()
G2 = nx.Graph()

# Add the same edges to both graphs
e = [(0, 1), (1, 2), (2, 0)]
G1.add_edges_from(e)
G2.add_edges_from(e)

# Check if the graphs could be isomorphic
result = nx.fast_could_be_isomorphic(G1, G2)

print('Graphs G1 and G2 could be isomorphic:', result)
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