<｜begin▁of▁sentence｜>### Instruction:
Imagine we're in a taxidermy workshop arranging a new exhibit that features a collection of specimens representing an ecosystem. The display is designed to show the relationships between different species, similar to creating an intricate and balanced diorama. To mirror the symbiotic and predatory interactions, we've crafted a network of connections amongst the species, akin to an ecosystem's food web.

We're presented with the task of dividing this complex web into two interconnected yet distinct subgroups or 'blocks', to better organize the exhibit and enhance the visual appeal. This 'web' is composed of relationships or 'edges' connecting pairs of species, forming partnerships equivalent to the following pairs: [(1, 2), (2, 3), (1, 3), (3, 4), (4, 5), (5, 6), (4, 6)]. Our goal is to employ a method akin to the Kernighan–Lin partitioning principle to find a balance that showcases the connections while maintaining ecosystem integrity.

Given we have not pre-selected which species to start with in each subgroup, we're open to experimenting with the arrangement, allowing up to 10 cycles of trial and error, akin to carefully adjusting the display until a satisfactory visual and thematic composition is achieved. Additionally, to ensure that our layout could be reproduced or revisited in the future, much like a careful scientific record in taxidermy, we'll set our random 'shuffle' of species to a consistent pattern, using a seed value of 42. 

To put it technically, could we harness the Kernighan–Lin algorithm in NetworkX to perform this task, letting it run for up to 10 iterations without edge weights, and ensuring reproducibility through the specified seed? How might this be documented as if it were a critical step in preparing the exhibit?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (1, 3), (3, 4), (4, 5), (5, 6), (4, 6)])

# Partition the graph using Kernighan-Lin algorithm
partition = nx.algorithms.community.kernighan_lin.kernighan_lin_bisection(G, partition=None, max_iter=10, weight='weight', seed=42)

print('Partition:', partition)
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