<｜begin▁of▁sentence｜>### Instruction:
As a copywriter working for a data analytics company specializing in social network analysis, you're tasked with creating marketing content that highlights the application of the DANMF model on real-world social network data. Imagine crafting a blog post or promotional material to explain how the DANMF model can uncover insights from complex networks, using the "Les Misérables" dataset as an example.

In your content, you'll need to explain how the DANMF model, a cutting-edge algorithm, can reveal hidden structures within networks like the characters' relationships in "Les Misérables." You'll describe how users can utilize the model with specific parameters such as layers=[32, 8], pre_iterations=100, iterations=100, seed=42, and lamb=0.01, to obtain node embeddings and community memberships from the graph data.

Your goal is to convey the technical aspects of the DANMF model in a compelling and accessible manner, emphasizing its ability to extract meaningful insights from network data for various applications such as social network analysis, recommendation systems, and community detection.
### Response:
import networkx as nx
from karateclub import DANMF

# Create an example graph
g = nx.les_miserables_graph()

# Initialize the DANMF model with custom parameters
model = DANMF(layers=[32, 8], pre_iterations=100, iterations=100, seed=42, lamb=0.01)

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

# Retrieve the embeddings and memberships
embeddings = model.get_embedding()
memberships = model.get_memberships()

print('Embeddings:\n', embeddings)
print('Memberships:\n', memberships)
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