<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a wildlife biologist conducting a study on the social structures of two separate populations of an animal species, represented by two distinct habitats. To model the interactions within these populations, you've created two ecological networks. The first habitat, Habitat A, includes three individual animals tagged as individuals 1, 2, and 3, with observed interactions documented as encounters between the pairs (1, 2), (2, 3), and (3, 1). Similarly, in Habitat B, you've labeled three different animals as individuals 4, 5, and 6, recording their interactions as encounters between pairs (4, 5), (5, 6), and (6, 4).

In an effort to determine if these two habitats exhibit equivalent social structures, despite being geographically separated, would you utilize the NetworkX library's `is_isomorphic` function to compare the underlying structure of these interaction networks for isomorphism? If isomorphism is present, it could suggest a shared social pattern across both populations, despite the potential variation in their environmental contexts.
### Response:
import networkx as nx
from networkx.algorithms.isomorphism import is_isomorphic

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

# Add nodes and edges to the first graph
G1.add_nodes_from([1, 2, 3])
G1.add_edges_from([(1, 2), (2, 3), (3, 1)])

# Add nodes and edges to the second graph
G2.add_nodes_from([4, 5, 6])
G2.add_edges_from([(4, 5), (5, 6), (6, 4)])

# Check if the two graphs are isomorphic
result = is_isomorphic(G1, G2)
print('G1 and G2 are isomorphic:', result)
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