<｜begin▁of▁sentence｜>### Instruction:
Imagine you are responsible for optimizing the collaborative efficiency within a diverse organization. You have a network of five key divisions represented by nodes [1, 2, 3, 4, 5], and their interdivisional collaborations are symbolized by the relationships [(1, 2), (2, 3), (3, 4), (4, 5), (2, 5)]. Now, you're tasked with identifying a set of these collaborative pairs which ensures that every division within the network either has a direct collaboration or is closely linked to another that does. This optimal subset of collaborations would result in the most effective and efficient team dynamic with minimal overlap. How would you utilize NetworkX to deduce this crucial set of connections for the organization's operational benefit while keeping the existing structure intact?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add some nodes and edges
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5), (2, 5)])

# Find minimum edge dominating set using NetworkX function
min_edge_dom_set = nx.min_edge_dominating_set(G)

print("Minimum edge dominating set:", min_edge_dom_set)
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