<｜begin▁of▁sentence｜>### Instruction:
As a laboratory technician, you are working on a project that involves analyzing the connectivity preferences among different types of biological molecules in a cellular network. Specifically, you have a set of 100 molecules categorized into three types: proteins ('A'), lipids ('B'), and carbohydrates ('C'). You also have a probability matrix that specifies the likelihood of connections between molecules of the same type and molecules of different types.

To support your research, you are looking to create a preference graph using this information, which will help visualize and analyze the interconnections between different types of molecules in the network. This graph will be valuable in understanding the patterns of connectivity and identifying potential areas of interest for further investigation.

You are seeking an approach, possibly using the NetworkX library in Python, to apply the probability matrix to generate this preference graph, allowing you to gain insights into the connectivity preferences within the cellular network.
### Response:
import igraph as ig
import json

# Define vertex types and their connection probabilities
vertex_types = ['A', 'B', 'C']
prob_matrix = [[0.2, 0.5, 0.1],
               [0.5, 0.7, 0.3],
               [0.1, 0.3, 0.8]]

# Generate a preference graph
G = ig.Graph.Preference(n=100, types=vertex_types, matrix=prob_matrix)

# Print out the summary
print(G.summary())
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