<｜begin▁of▁sentence｜>### Instruction:
As a project scheduler overseeing the workflow within an intricate project, imagine we have a network of tasks and dependencies, structured as follows:

- Task transitions are between task 0 to 1, 1 to 2, 1 to 5, 5 to 4, 2 to 4, 2 to 3, 4 to 3, and 3 to 6.

To optimize our resources and timeline, we need insight into the influence each task has on the flow of work across our project network. Specifically, could you apply your expertise to assess the communicability betweenness centrality for each node in this project network, utilizing NetworkX as our processing tool? This measurement will help us discern which tasks are most critical for the communication among others, ensuring we prioritize our attention and resources accordingly.
### Response:
import networkx as nx

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

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

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