<｜begin▁of▁sentence｜>### Instruction:
Imagine we're crafting a complex prosthetic network that replicates human social interactions within a community. Think of this network as an elaborate system of interconnected components, where each junction point represents an individual, and the rods or links between them symbolize the interactions they share.

Now, in our synthetic community model, we have specific interaction pathways: (0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), and (3, 6). To optimize the efficiency and responsiveness of the prosthetic, we need to assess the importance of each junction point - in other words, the 'communicability betweenness centrality' of each node - within the network of social interactions.

How could we, as prosthetists wanting to mirror natural social complexity, determine the communicability betweenness centrality for each individual junction in our prosthetic model? This would ensure that the system's design accurately reflects the nuanced interplay of communication within a real-life community, thus enabling a more lifelike function in the artificial network we are engineering.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
# Add edges
G.add_edges_from([(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)])

# Compute communicability betweenness centrality
cbc = nx.communicability_betweenness_centrality(G)

# Print communicability betweenness centrality for each node
for node in sorted(cbc):
    print(f"{node}: {cbc[node]:0.2f}")
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