<｜begin▁of▁sentence｜>### Instruction:
Absolutely, I'd be delighted to assist with your inquiry! Let's imagine we operate a customer service center that caters to two separate groups of clientele, each with its own unique set of characteristics and needs. We're looking to create a sophisticated model that allows us to understand and predict the likelihood of our agents interacting within each individual group as well as between the two different customer bases. Moreover, we want to take the insights we've gained from our current call data and use them to simulate potential new interactions, which would help us improve our service strategies. Now, suppose we have access to NetworkX, an API that specializes in the creation, manipulation, and study of complex networks. It offers the necessary tools for this kind of modeling and analysis, allowing us to map out and explore the complex web of our customer interactions. How might we proceed in using the patterns identified through NetworkX to foresee and better plan for future communication dynamics within our call center environment?
### Response:
from graspologic.models import SBMEstimator
import numpy as np
import networkx as nx
from graspologic.simulations import sbm

# Step 1: Create a stochastic block model graph
n_communities = 2
community_sizes = [50, 50]
p_matrix = [[0.5, 0.05], [0.05, 0.5]]

np.random.seed(42)
G = sbm(community_sizes, p_matrix)

# Convert to networkx graph for visualization, if needed
nx_graph = nx.Graph(G)

# Step 2: Fit the SBMEstimator to the graph
sbm_estimator = SBMEstimator(directed=False, loops=False)
sbm_estimator.fit(G)

# Step 3: Analyze the estimated model
block_prob_matrix = sbm_estimator.block_p_
print('Block probability matrix:', block_prob_matrix)

# Step 4: Optionally, you can sample new graphs from the estimated model
n_samples = 3
sampled_graphs = [nx.Graph(g) for g in sbm_estimator.sample(n_samples)]

# Print metadata, such as vertex assignments to blocks
vertex_assignments = sbm_estimator.vertex_assignments_
print('Vertex assignments:', vertex_assignments)
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