<｜begin▁of▁sentence｜>### Instruction:
As a seasoned network scientist, I specialize in analyzing complex structures to uncover key insights. Suppose you have a network representing connections between cities for transportation planning. Now, if a scenario emerges where a cycle is identified within this network, the challenge is to transform it into a tree-like structure for more efficient routing. To tackle this, we can employ a Breadth-First Search (BFS) algorithm starting from a selected central city as the root node. This technique can help us unfold the cyclic network into a tree, thereby optimizing travel routes and resource allocation within the city infrastructure.
### Response:
from igraph import Graph

# Define a graph with a cycle
g = Graph(edges=[(0, 1), (1, 2), (2, 3), (3, 0)])

# Perform unfolding of the graph to create a tree
unfolded_tree = g.unfold_tree(mode='BFS', root=0)

print(unfolded_tree)
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