<｜begin▁of▁sentence｜>### Instruction:
Imagine you're coordinating with local communities to build a network of support, with the goal of ensuring relief reaches each sector efficiently. We want to create a simplified model to visualize potential collaboration paths. To achieve this, we plan to simulate a community support network using a graph where each of the 10 key local groups represents a node.

The interactions between these groups are random, with a 50% chance that any two groups will collaborate. However, we must be able to replicate this network simulation exactly, to compare different relief distribution strategies reliably.

Could you craft a scenario in which we generate such a model using NetworkX, and apply a fixed seed to our random number generator for consistent results? If we decide to store this network structure, it would be kept in a .gml file for ease of sharing with our field teams.
### Response:
import networkx as nx

# Create a G_{n,p} random graph with 10 nodes and probability of 0.5 for edge creation
G = nx.binomial_graph(n=10, p=0.5, seed=42)

# Print the generated graph
nx.draw(G, with_labels=True)
plt.show()
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