<｜begin▁of▁sentence｜>### Instruction:
As an ergonomist, let's consider a scenario where you're tasked with optimizing the layout of a workspace to enhance collaboration among team members. You have a network of desks connected by pathways representing the flow of communication and interaction between colleagues. Imagine you have an undirected graph representing this network, where each edge represents a pathway between desks.

Now, your goal is to assess the betweenness centrality of each pathway to identify key communication routes within the workspace. To achieve this, you can utilize the `edge_betweenness` function provided by the `igraph` API. This function calculates the betweenness centrality for all edges in the network, allowing you to pinpoint crucial pathways where information flows most frequently. By understanding these communication hubs, you can strategically arrange desks and optimize the workspace layout to foster greater collaboration and efficiency among team members.
### Response:
import igraph as ig

# Create an igraph Graph
graph = ig.Graph(edges=[(0, 1), (0, 2), (1, 2), (2, 3), (3, 4)])

# Calculate edge betweenness
edge_betweenness = graph.edge_betweenness()

# Print the edge betweenness values
print(edge_betweenness)
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