<｜begin▁of▁sentence｜>### Instruction:
As a restaurant manager overseeing operations, including staffing and customer service, you might be familiar with the importance of arranging tables and seating in a visually appealing and efficient manner to enhance the dining experience for your customers. Similarly, in the realm of network analysis, we often seek to visually represent data in a clear and aesthetically pleasing way. Suppose you have a scenario where you're hosting a special event in your restaurant with 10 VIP guests, each representing a node in a network. To facilitate networking and conversation, you want to create a visually appealing layout for their seating arrangement. Now, imagine there's a tool called GraphOpt, similar to managing staffing or finances, that helps optimize the layout of nodes in a graph. How would you utilize GraphOpt to determine the positions of these VIP guests in a way that maximizes visual appeal and facilitates interaction among them?
### Response:
from igraph import Graph

# Generate a simple graph
g = Graph.Erdos_Renyi(n=10, p=0.5)

# Run the graphopt layout algorithm
layout = g.layout_graphopt(niter=500, node_charge=-30, node_mass=20,
                             spring_length=50, spring_constant=0.01,
                             max_sa_movement=5)

# Plot the graph using the layout
ig.plot(g, layout=layout)
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