<｜begin▁of▁sentence｜>### Instruction:
As an agronomist specializing in optimizing crop layouts for improved yield and sustainability, I'm exploring graph theory to plan the arrangement of different crop varieties in a field. Imagine you have a field divided into plots, and each plot represents a node in a graph. The edges between nodes signify factors like irrigation channels or wind flow that influence crop growth.

Now, suppose you have a graph representing these relationships with the following edges: [(0, 1), (0, 2), (1, 3), (2, 3)]. You want to visualize this graph to better understand how different areas of the field interact. Can you utilize an algorithm, perhaps provided by the NetworkX library, that automatically chooses an optimal layout based on the topological properties of the graph? This will help us visualize the field's dynamics and plan our crop arrangements more effectively.
### Response:
import igraph as ig

# Create a graph
edges = [(0, 1), (0, 2), (1, 3), (2, 3)]
graph = ig.Graph(edges=edges)

# Apply a layout algorithm automatically based on topological properties
layout = graph.layout_auto()

# Plot the graph using the layout
ig.plot(graph, layout=layout, vertex_label=graph.vs['name'])
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