<｜begin▁of▁sentence｜>### Instruction:
In the fascinating world of Lepidopterology, imagine embarking on a study to compare two distinct habitats defined by their own intricate networks of interaction. Picture the first habitat as a cyclical environment hosting six species where each species interacts with two neighbors, forming a perfect loop akin to a circle in nature. We'll refer to this as Habitat G1 for our purposes.

Now, envisage the second habitat, Habitat G2, which is structured like a wheel hub, with a central species that directly interacts with six surrounding species, and each of these peripheral species interacts with its immediate neighbors, forming a wheel with seven total species.

Our objective is to assess the dissimilarity and transformation requirements between Habitat G1 and Habitat G2 by computing the Graph Edit Distance (GED). To add realism to our examination, we acknowledge that species are identified by their unique attributes. For this study, a custom node substitution cost function will come into play, where we ascribe a cost of 1 when comparing two species (nodes) with differing attributes and 0 when their attributes align.

Your task involves delving into this ecological analysis—but without actual calculation or guidance on methods—simply rephrase this scenario to maintain the core semantics while reducing the abstraction. Regrettably, we do not possess a GML file or additional data for this theoretical exercise.
### Response:
import networkx as nx

# Create two graphs
G1 = nx.cycle_graph(6)
G2 = nx.wheel_graph(7)

# Define a custom node substitution cost function
def node_subst_cost(node1_attr, node2_attr):
    return 1 if node1_attr != node2_attr else 0

# Calculate the Graph Edit Distance between G1 and G2 with custom node substitution cost
ged = nx.graph_edit_distance(G1, G2, node_subst_cost=node_subst_cost)

print(f'Graph Edit Distance between G1 and G2: {ged}')
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