<｜begin▁of▁sentence｜>### Instruction:
Imagine you're preparing a bid for a construction project, where the project is the establishment of two new residential zones of equal population. Your task includes mapping out the potential social dynamics of the future residents. In each of these residential zones, the chances of any two neighbors knowing one another are quite similar to the likelihood of encountering common building materials in similar housing projects — let's say around 10%. Meanwhile, the odds of a resident from one neighborhood being acquainted with a resident from the other is more akin to the chances of dealing with a supplier outside your usual network — about 5%.

Your objective is to draft a blueprint that represents these relationships, essentially a 'social architecture' diagram if you will, using networkx as your planning tool. Along with the structure, the blueprint should classify the residents according to their neighborhood affiliations, much like labeling different areas of a construction site. How would you go about creating this diagram to visually and analytically represent the community connections while also clearly identifying each individual's community zone?
### Response:
import graspologic
import numpy as np

# Define the block sizes and inter-block connection probabilities
n = [50, 50] # Two communities, each with 50 nodes
p = [[0.1, 0.05], [0.05, 0.1]] # Inter-block connection probabilities

# Generate a stochastic block model graph
A, labels = graspologic.simulations.sbm(n, p, return_labels=True)

# Using the generated graph (A) and community labels (labels)
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