<｜begin▁of▁sentence｜>### Instruction:
As an architect designing a complex of interconnected office buildings, you've been tasked with analyzing the spatial organization within the network of corridors and rooms. One particular building has 100 rooms connected by 500 doors, creating a dynamic flow of movement among its occupants.

To understand the natural divisions and clusters within this network, you plan to utilize an advanced tool called "community_fastgreedy" from the architectural analysis software suite. This tool employs cutting-edge algorithms to identify and delineate distinct communities within the network based on the frequency and intensity of interactions between rooms.

Once the analysis is complete, you aim to visualize the results by assigning each room a unique color corresponding to its community membership. This visual representation will not only aid in comprehending the spatial organization but also inform future design decisions aimed at optimizing efficiency and collaboration within the building complex.
### Response:
import igraph as ig

# Assuming you have a graph `g` to plot
# Typically you would read this graph from a file or generate it
# For this example, let's create a simple graph
g = ig.Graph.Erdos_Renyi(n=100, m=500)

# Find the community structure
fg = g.community_fastgreedy()

# Convert it into a flat clustering and print the membership
clusters = fg.as_clustering()
print(clusters.membership)

# Visualize the graph with the membership as vertex colors
# Assign a color to each membership
# e.g., if there are 3 communities,[0, 1, 2], then three colors could be 'red', 'green', 'blue'
color_dict = {0: 'red', 1: 'green', 2: 'blue'}
visual_style = {}
visual_style['vertex_color'] = [color_dict[membership] for membership in clusters.membership]

# Plot the graph
ig.plot(g, **visual_style)
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