<｜begin▁of▁sentence｜>### Instruction:
As a gerontologist focusing on elderly care and well-being, imagine you are part of a team working on a research project to analyze social connections among a group of 100 older adults in a retirement community. The team has collected data on the relationships between these individuals, resulting in a network with 100 nodes and 500 edges. Now, you are tasked with using the Walklets model, as described in the ASONAM ‘17 paper 'Don’t Walk, Skip! Online Learning of Multi-scale Network Embeddings', to generate a 64-dimensional embedding of the nodes in the social network. This embedding will help uncover patterns in the social connections and could potentially aid in understanding the impact of social interactions on the well-being of the residents.
### Response:
import networkx as nx
from karateclub import Walklets

# Create a graph
G = nx.gnm_random_graph(n=100, m=500)

# Initialize Walklets model with specified hyperparameters
model = Walklets(walk_number=5, walk_length=40, dimensions=64, workers=2, window_size=5, epochs=3, learning_rate=0.01, min_count=0, seed=42)

# Fit the model
model.fit(G)

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