<｜begin▁of▁sentence｜>### Instruction:
Imagine we're mapping out the social network of an ensemble cast for an upcoming blockbuster movie, where the connections between the actors represent their on-set interactions. The cast includes members associated with various scenes, as follows: [(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)]. To ensure we maximize the potential for on-screen chemistry and off-screen promotional appearances, we're interested in gauging the level of rapport—or "communicability"—each pair of our talented clients might naturally develop. Could we utilize NetworkX to analyze this web of interactions and determine the communicability scores for all our actor pairings based on the interaction data provided? This would help us in understanding the potential dynamic within the cast and establishing the most effective pairings for interviews and media events.
### Response:
import networkx as nx

# Create an undirected 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)])

# Compute communicability between all pairs of nodes
comm = nx.communicability(G)

print('Communicability between all pairs of nodes:')
for node, comm_values in comm.items():
    print(f'{node}: {comm_values}')
<|EOT|>
