<｜begin▁of▁sentence｜>### Instruction:
As a product designer, you may be tasked with analyzing user behavior data to understand the interaction patterns between different user groups. Network analysis could help to uncover how users from different demographics and behavior patterns are connected within a digital platform. By utilizing the stochastic block model, you can create a network graph to represent these connections and identify distinct clusters or communities. This approach would involve specifying connection probabilities based on the preference matrix, effectively revealing the underlying structure of user interactions without self-connections. This analysis can then inform the design and development of products to better cater to the needs and preferences of diverse user groups.
### Response:
from igraph import Graph

# Parameters for the SBM function
n = 100  # Number of vertices
block_sizes = [25, 75]  # Sizes of the two blocks
pref_matrix = [[0.1, 0.02], [0.02, 0.05]]  # Connection probabilities for vertex types
directed = False  # Generate an undirected graph
loops = False  # Do not allow loop edges

# Generate the graph with the SBM function
g = Graph.SBM(n, pref_matrix, block_sizes, directed=directed, loops=loops)

# Plot the graph (optional)
igraph.plot(g)
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