<｜begin▁of▁sentence｜>### Instruction:
As a political campaign manager, you're tasked with leading and managing a political campaign for a candidate. Imagine that you have a team of 100 staff members, each responsible for connecting with 3 local neighborhoods. With a strategy to adapt to new opportunities, there's a 50% chance of reassigning staff members to different neighborhoods. Additionally, the team has collected a dataset with 100 records of voter preferences on 32 key issues, with binary indicators generated randomly based on a threshold of 0.15.

Given these circumstances, can the FSCNMF model be used to analyze both the connections between neighborhoods and the voter preferences in order to create a comprehensive strategy that accounts for both the geographical structure and the issue preferences of the voters?
### Response:
import networkx as nx
from karateclub import FSCNMF
from scipy.sparse import coo_matrix
import numpy as np

# Create a graph
G = nx.newman_watts_strogatz_graph(100, 3, 0.5)

# Create a feature matrix
features = coo_matrix((np.random.rand(100, 32) > 0.85).astype(int))

# Initialize FSCNMF
model = FSCNMF(dimensions=16, lower_control=1e-15, iterations=200, alpha_1=500.0, alpha_2=0.5, alpha_3=0.5, beta_1=500.0, beta_2=0.5, beta_3=0.5)

# Fit the model
model.fit(G, features)

# Get the embedding
embedding = model.get_embedding()
print(embedding)
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