<｜begin▁of▁sentence｜>### Instruction:
Imagine a family tree represented as a network, where each individual is a node and the connections between relatives are edges. We've mapped out a portion of this tree including four family members represented by nodes {0, 1, 2, 3}. The connections—symbolizing shared genetic information—are as follows: {(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)}. To better understand the intricacies of genetic sharing and potential condition propagation within this family network, I'm interested in assessing the average global efficiency—a measure of how effectively information is exchanged across the entire network. Could you assist in computing this value using NetworkX, ensuring the result is precise to 12 decimal places? This will help in evaluating how closely related the individuals are and could provide insights when discussing genetic risks and hereditary connections with the family.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add edges to the graph
G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])

# Calculate the global efficiency of the graph
efficiency = nx.global_efficiency(G)

# Output the average global efficiency (rounded to 12 decimal places)
print(round(efficiency, 12))
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