<｜begin▁of▁sentence｜>### Instruction:
Imagine we're developing a risk analysis model where different points of assessment are arranged in a hexagonally connected grid, akin to risk nodes in an insurance network. Our current task involves constructing a simplified representation of this network with a specific structure.

We need to construct a hexagonally tessellated lattice as our network framework, consisting of exactly 2 rows and 3 columns. This lattice shouldn't wrap around upon itself; hence, periodicity must be avoided to prevent creating an infinite loop of risk assessment nodes.

For visualization purposes, we'll employ matplotlib to render the network graphically. It's critical to have each node's position explicitly defined in the visualization to make the analysis and interpretation straightforward. There's no need to worry about a GML file in this scenario, and let's exclude any extraneous data to keep our focus on the core structural representation.

To put it simply, our goal is to design a non-periodic, hexagonal lattice graph featuring 2 rows and 3 columns of risk nodes, and then proceed to visually map out this network using matplotlib, including clear indicators of each node's specific location within the structure.
### Response:
import networkx as nx

# Generate a hexagonal lattice graph
m = 2  # rows
n = 3  # columns
periodic = False  # non-periodic
with_positions = True  # include node positions in 'pos'
G = nx.hexagonal_lattice_graph(m, n, periodic=periodic, with_positions=with_positions)

# Get the node positions for plotting
pos = nx.get_node_attributes(G, 'pos')

# Draw the graph
import matplotlib.pyplot as plt
nx.draw(G, pos, with_labels=True, node_size=700, node_color='lightblue')
plt.show()
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