<｜begin▁of▁sentence｜>### Instruction:
In the bustling environment of a corporate health and safety department, we're constantly exploring innovative ways to ensure the well-being of our workforce. Imagine the company's social network as a complex structure where each department is a close-knit group, similar to two separate cliques, with both groups essential to seamless operation yet functioning independently - akin to a connected barbell graph with two cliques, each comprised of 5 individuals, with no direct linkages between them.

In an effort to better understand the intricacies of this dual-clique employee network and to potentially enhance inter-departmental synergy, we are considering running an analysis analogous to computing the Fiedler vector of such a graph. This approach is much like assessing the fault lines in a solid structure to mitigate risks; determining the Fiedler vector will help us pinpoint where the fault lines in communication and collaboration might exist within our organizational network. 

Could you propose a method for computing the Fiedler vector in this context using the NetworkX software, thereby empowering us to proactively address any underlying issues in our company's connectedness and resiliency?
### Response:
import networkx as nx

# Create a graph
G = nx.barbell_graph(5, 0)

# Calculate the fiedler vector
fiedler_vector = nx.fiedler_vector(G, normalized=True, seed=1)

print('Fiedler vector:', fiedler_vector)
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