<｜begin▁of▁sentence｜>### Instruction:
Given a karate_club_graph, can you use the SocioDim model to generate node embeddings for the graph based on the largest eigenvalues of the graph modularity matrix, as described in the KDD ‘09 paper “Relational Learning via Latent Social Dimensions”? What are the resulting embeddings using the default parameters of dimensions=128 and seed=42?
### Response:
import networkx as nx
from karateclub import SocioDim

# Create a graph using NetworkX
G = nx.karate_club_graph()

# Initialize a SocioDim model with the desired dimensions and seed
model = SocioDim(dimensions=128, seed=42)

# Fit the model to the graph
model.fit(G)

# Retrieve the node embeddings
embedding = model.get_embedding()

print(embedding)
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