<｜begin▁of▁sentence｜>### Instruction:
Given a Newman-Watts-Strogatz graph with 100 nodes, where each node is connected to 3 nearest neighbors in a ring topology, and each edge is rewired with a probability of 0.5, and a random 4-dimensional feature matrix for node attributes, how can the SINE model ('Scalable Incomplete Network Embedding') be used to obtain node embeddings?
### Response:
import networkx as nx
from karateclub import SINE
import numpy as np
from scipy.sparse import coo_matrix

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

# Generate random node features
features = np.random.rand(100, 4)
feature_matrix = coo_matrix(features)

# Instantiate and fit the SINE model
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)
model.fit(G, feature_matrix)

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