<｜begin▁of▁sentence｜>### Instruction:
Imagine you're cataloging the intricate interactions within two separate butterfly habitats, G and H. For habitat G, you've meticulously charted the symbiotic relationships between butterfly species through direct observations, resulting in connections akin to a pattern woven in nature: [Monarch-Danaus plexippus & Swallowtail-Papilio machaon (0, 1), Monarch-Danaus plexippus & Painted Lady-Vanessa cardui (0, 2), Swallowtail-Papilio machaon & Painted Lady-Vanessa cardui (1, 2), Swallowtail-Papilio machaon & Red Admiral-Vanessa atalanta (1, 3)].

In the second habitat, H, through equally diligent studies, different relationships emerge, with a unique set of interactions represented by the connections: [Monarch-Danaus plexippus & Swallowtail-Papilio machaon (0, 1), Swallowtail-Papilio machaon & Painted Lady-Vanessa cardui (1, 2), Monarch-Danaus plexippus & Mourning Cloak-Nymphalis antiopa (0, 3)].

Your objective now is to discern the exclusive interactions that are present in the first habitat G but absent in H. This task will involve creating a new register that effectively highlights these unique connections. In essence, we're sifting through the data from both habitats to isolate the symbiotic patterns that define the exclusive ecological traits of habitat G. Could you compile these distinctive links of habitat G into a new ledger, effectively subtracting the commonality found within habitat H?
### Response:
import networkx as nx

# Create graph G
G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3)])

# Create graph H
H = nx.Graph([(0, 1), (1, 2), (0, 3)])

# Compute the difference between G and H
R = nx.difference(G, H)

print('Nodes in R:', list(R.nodes))
print('Edges in R:', list(R.edges))
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