<｜begin▁of▁sentence｜>### Instruction:
As a researcher examining the intricate web of interactions within a local academic institution, you're preparing to launch an insightful inquiry into the composition and dynamics of student relations. Your focus hones in on the institution's prevailing cliques—precisely, two dominant social assemblies, referred to as Group A and Group B. Each faction is composed of a cohort of 50 scholars, summing up to a collective of 100.

You propose a compelling theory: affiliations within identical clusters are presumed to have a denser concentration of social exchanges compared to those spanning across distinct assemblages. To substantiate this conjecture, you're considering deploying an analytical model, the stochastic block model (SBM), which serves as a formidable instrument to delineate such social architectures.

Your endeavor will employ the functionality of `graspologic.simulations.sbm`, a computational tool, to construct a paradigm of the social network, where nodes embody the young academics, and the edges symbolize their interactions. The model's parameters—namely, the interaction probabilities—are set apart, distinguishing intra-group from inter-group communication propensities.

Visual aids are essential to extrapolate and examine the nuances of the model. To this end, networkx will be instrumental in crafting a visual representation of this complex social network, enabling an intuitive exploration of the interwoven connections between the pupils.

Embedding the information into a practical scenario, the simulation exercise will configure a digital landscape of the school's social framework, providing empirical evidence to either bolster or refute your hypothesis of socio-centric tendencies within these academic cohorts.
### Response:
import numpy as np
from graspologic.simulations import sbm
import networkx as nx

# Parameters for SBM
n_communities = [50, 50] # number of nodes in two communities
p_matrix = [[0.1, 0.01], [0.01, 0.1]] # block model probabilities

# Generate a stochastic block model graph
adj_matrix, labels = sbm(n_communities, p_matrix, return_labels=True)

# Convert to a networkx graph
G = nx.from_numpy_array(adj_matrix)

# Plot the networkx graph using spring layout
import matplotlib.pyplot as plt
pos = nx.spring_layout(G)
nx.draw(G, pos, node_color=labels, with_labels=True)
plt.show()
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