<｜begin▁of▁sentence｜>### Instruction:
Given a Newman-Watts-Strogatz small-world graph with 50 nodes, each connected to 5 nearest neighbors and a rewiring probability of 0.05, and using identity matrix as features for the nodes, how can the SINE model be employed to derive the embeddings for this graph with the following hyperparameters: 10 random walks per node, walk length of 80, embedding dimensionality of 128, using 4 workers, window size of 5 for the power matrix, running for 1 epoch, with a HogWild! learning rate of 0.05, and a random seed of 42?
### Response:
import networkx as nx
import numpy as np
from karateclub import SINE
from scipy.sparse import coo_matrix

# Create a graph
G = nx.newman_watts_strogatz_graph(50, 5, 0.05)

# Initialize the feature matrix as an identity matrix
features = np.identity(50)
row = np.arange(50)
col = np.arange(50)
feature_matrix = coo_matrix((np.ones(50), (row, col)), shape=(50, 50))

# Initialize SINE model with the specified hyperparameters
model = SINE(walk_number=10, walk_length=80, dimensions=128, workers=4, window_size=5, epochs=1, learning_rate=0.05, min_count=1, seed=42)

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

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