<｜begin▁of▁sentence｜>### Instruction:
Suppose I've got a bit of a unique request from some chaps interested in the structure of social networks, and they're intrigued by the idea of a community where each individual has a certain number of connections that ain't equal across the board. They fancy a model where the number of acquaintances each person has follows some kind of mathematical rule, something they called a "power law distribution." So, let's say they want this modeled community to have a set number of folks -- we'll call that our "target number of nodes." And there's this specific detail about how steeply the number of acquaintances drops off as you look at folks with more and more connections, which they've got a fancy term for: the "exponent for the power law distribution."

Now, what they're asking me for, in barber terms, is how one can craft a sort of diagram using that NetworkX tool to illustrate this hypothetical social network where the number of pals each person has follows this uneven pattern, making sure that the diagram includes the precise number of people they're aiming for and that the connections between folks reflects this power law rule. How would someone go about setting up such a scenario with NetworkX?
### Response:
import networkx as nx

n = 10  # Number of nodes
alpha = 2  # Exponent for the power law distribution

sequence = nx.utils.powerlaw_sequence(n, alpha)
print('Power law sequence:', sequence)

G = nx.expected_degree_graph(sequence, selfloops=False)

print(G.edges())
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