<｜begin▁of▁sentence｜>### Instruction:
As an inventory planner, you are tasked with optimizing stock availability and reducing carrying costs by analyzing sales data and forecasting demand. Leveraging network analysis can provide valuable insights into supply chain dynamics and dependencies. For example, utilizing the 'igraph' library with a 'matplotlib' backend allows you to visually represent the network of suppliers, warehouses, and transportation routes. This can aid in identifying critical nodes and paths within the supply chain. Additionally, you can specify visual properties such as vertex size, color, and edge width to highlight key elements of the network, contributing to a comprehensive understanding of inventory flow and potential bottlenecks.
### Response:
import igraph as ig
import matplotlib.pyplot as plt

# Instantiate graph and add vertices and edges
g = ig.Graph()
g.add_vertices(3)
g.add_edges([(0, 1), (1, 2)])

# Define visual style
visual_style = {}
visual_style['vertex_size'] = 20
visual_style['vertex_color'] = 'blue'
visual_style['vertex_label'] = ['A', 'B', 'C']
visual_style['edge_width'] = 2

# Plot the graph
plot = ig.plot(g, **visual_style)
plt.show()
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