<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting an experimental performance piece titled "Connections in the Ether," where you explore the concept of serendipity in human interactions within a closed network of 10 individuals. Each participant in this social experiment represents a node, and the fleeting, chance encounters between them are symbolized by edges. The twist? Any given encounter between two participants only has a 20% chance of occurring, mirroring the unpredictability and randomness of real-life connections.

Your vision is to conduct this performance 1000 times, each iteration generating a unique pattern of interactions. The heart of your piece lies in understanding the essence of communal closeness, quantified by what you've learned is called the "average clustering coefficient" - a measure of the degree to which nodes in a network tend to cluster together. 

How would you go about estimating the average clustering coefficient of these encounters in your performance piece across all 1000 variations, to capture the underlying narrative of interconnectedness in human relationships?
### Response:
import networkx as nx
from networkx.algorithms import approximation

# Create an Erdős-Rényi graph with 10 nodes and probability of edge creation 0.2
G = nx.erdos_renyi_graph(10, 0.2, seed=10)

# Estimate the average clustering coefficient using 1000 trials
average_clust_coef = approximation.average_clustering(G, trials=1000, seed=10)

print('Estimated average clustering coefficient:', average_clust_coef)
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