<｜begin▁of▁sentence｜>### Instruction:
"As a journalist with a knack for uncovering the underlying patterns in social networks, I've recently come across an intriguing scenario. Imagine a tightly-knit group, composed of five key players, each connected to every other with a level of communication that's nothing short of complete. Given the nature of this group, where everyone evidently shares information with one another, I'm curious about the degree of interconnectedness present in this network. Utilizing the analytical tool provided by NetworkX, could we quantify this interconnectedness? More specifically, I'm interested in learning how the transitivity measure, which NetworkX can calculate, would reflect the group's cohesiveness. This would provide valuable insights into the dynamics within such a well-connected community, enabling us to better understand how information might flow within similar networks."
### Response:
import networkx as nx

# Create a complete graph with 5 nodes
G = nx.complete_graph(5)

# Compute the transitivity of the graph
transitivity_value = nx.transitivity(G)

print('Transitivity of the graph:', transitivity_value)
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