<｜begin▁of▁sentence｜>### Instruction:
Imagine you're managing the complex network of an e-commerce platform, where each node represents a distribution center, and the edges symbolize direct shipping routes between these centers. Your network comprises routes like [(1, 2), (2, 3), (3, 1), (3, 4), (4, 5), (5, 3), (5, 6), (6, 4), (1, 7), (7, 8)]. You want to ensure that your distribution system is robust and capable of handling various levels of disruptions. In this case, a k-edge-connected component is a subnetwork where, even if up to (k-1) shipping routes were disrupted, all the centers in this component could still reach each other.

Your mission is to use NetworkX's EdgeComponentAuxGraph tool to assess the integrity of your distribution network by finding all subnetworks that are connected with redundancy levels ranging from basic connectivity (1-edge-connected) to higher resilience (3-edge-connected). This analysis will help you develop strategies to mitigate the impact of possible route disruptions, ensuring that your online sales and operations continue to run smoothly without significant delays or interruptions in service.
### Response:
import networkx as nx
from networkx.algorithms.connectivity import EdgeComponentAuxGraph

# Build a graph with multiple levels of k-edge-connected components
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 1), (3, 4), (4, 5), (5, 3), (5, 6), (6, 4), (1, 7), (7, 8)])

# Construct the auxiliary graph
eca = EdgeComponentAuxGraph.construct(G)

# Query the k-edge-connected components with k=1
components_k1 = sorted(map(sorted, eca.k_edge_components(k=1)))
print(f'1-edge-connected components: {components_k1}')

# Query the k-edge-connected components with k=2
components_k2 = sorted(map(sorted, eca.k_edge_components(k=2)))
print(f'2-edge-connected components: {components_k2}')

# Query the k-edge-connected components with k=3
components_k3 = sorted(map(sorted, eca.k_edge_components(k=3)))
print(f'3-edge-connected components: {components_k3}')
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