<｜begin▁of▁sentence｜>### Instruction:
Given a Newman-Watts-Strogatz small-world graph with 100 nodes, each connected to 3 nearest neighbors and with a 0.5 probability of adding a new edge, how can you use the GraRep model to learn node embeddings that capture global structural information?
### Response:
import networkx as nx
from karateclub import GraRep

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

# Initialize GraRep model
model = GraRep(dimensions=32, iteration=10, order=5, seed=42)

# Fit the model
model.fit(G)

# Get the embedding
embedding = model.get_embedding()
print(embedding)
<|EOT|>
