<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working in the bustling world of sales, and in your network of contacts and potential leads, connections between individuals are as valuable as the products you're pushing. You've got a list detailing which of your acquaintances know each other – let's think of it as a web of potential referrals. The connections look like this: Chuck is acquainted with Linda, Linda knows both Sam and Nancy, Nancy has a rapport with Ivan, Sam is on good terms with Ivan and Emma, Ivan and Emma both chat with Gary, and lastly, Gary has had a couple of golf games with Zack.

You want to optimize your strategy and figure out the likelihood of information flow between any two individuals in your network. In essence, you're trying to ascertain the communicability, which is a fancy term for the ease with which news (like the latest sales pitch or product launch) travels through the grapevine, touching every node of human contact along the way.

To put it in sales lingo, could you compute the potential for referral-generated business across all pairs in your contact list based on how well they're connected? This will help you understand who to target to get the word out most effectively about your new products. There's no GML file or external dataset here, just the relationships I've described. Can you take this abstract concept and put it into play for practical gains?
### Response:
import networkx as nx

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

# Compute the communicability
comm = nx.communicability_exp(G)

# Display the communicability between nodes
for node, values in comm.items():
    print(f'Communicability for node {node}: {values}')
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