<｜begin▁of▁sentence｜>### Instruction:
Imagine you're at a well-child visit discussing the intricate development stages of a child. Just as a child must hit certain growth milestones to ensure their healthy development, the flourishing of graphs in network analysis requires understanding their robust structures. Here, think of the Petersen graph as a unique child under our observation. We're curious to learn about its resilience and the strengths of its connections.

In pediatric terms, we could liken it to exploring the immune system's strength—how well a child can withstand common illnesses with at least a certain level of immunity, which we could consider as 'k'. Now, replacing 'immune system' with 'node connectivity', we want to figure out the most substantial groupings or 'communities' within this Petersen 'network' that share a minimum threshold 'k' level of interconnectedness. It's like understanding which age groups are most resistant to a specific flu strain.

Can you help unravel the network's "development stages" by analyzing which clusters within the Petersen graph are tightly knit enough to be considered separate 'k-components'? A 'k-component' here is akin to a tightly-bound group of children, where at least 'k' friendships must be disrupted for an individual to be isolated from the group. Utilize NetworkX for this analysis, as you would use your medical tools, to assess the support system and social resilience of the graph's structure.
### Response:
import networkx as nx
from networkx.algorithms import approximation as apxa

# Create a Petersen graph
G = nx.petersen_graph()

# Find the k-component structure
k_components = apxa.k_components(G)

print(k_components)
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