<｜begin▁of▁sentence｜>### Instruction:
As a Quality Assurance Manager overseeing the robustness and efficiency of communication channels within an organizational network, we are tasked with a quality check of the communicability across departments (represented as nodes) and their interconnections (represented as edges). We have a network comprising seven departments, and the interactions between them are as follows: Department 0 interacts with 1, Department 1 with both 2 and 5, Department 5 with 4, Department 2 with 4 and 3, Department 4 with 3, and Department 3 with 6.

To accurately measure and ensure quality in the ease of communication passing through each department, we are interested in computing the communicability betweenness centrality for each department node within this network structure.

Could you reframe this inquiry within the context of utilizing NetworkX to analyze the data, while ensuring that the semantics—pertaining to the calculation of the communicability betweenness centrality for each department node—are meticulously preserved? Please make sure the scenario encompasses the given network data and is translated into a practical application that fits within the scope of quality control metrics we employ.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add edges to the 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
for node in sorted(cbc):
    print(f"{node} {cbc[node]:0.2f}")
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