<｜begin▁of▁sentence｜>### Instruction:
As an antique dealer who specializes in rare books, you've recently come across a collection of 100 vintage manuscripts, each with unique historical significance. To better understand the connections between these manuscripts, you're interested in applying a modern technique called the BigClam model. This model, inspired by the work of Newman, Watts, and Strogatz, helps identify overlapping communities within networks. 

You're particularly intrigued by how these manuscripts might share common themes or authors, akin to communities within a social network. The BigClam model promises to provide node embeddings, essentially representing each manuscript in a multi-dimensional space, and cluster memberships, indicating which manuscripts belong to similar thematic groups.

Now, armed with this understanding, you're eager to apply the BigClam model to your collection, unraveling the hidden connections and uncovering the fascinating stories intertwined within these historical documents.
### Response:
import networkx as nx
from karateclub import BigClam

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

# Initialize the BigClam model
bigclam = BigClam(dimensions=8, iterations=100, learning_rate=0.01, seed=42)

# Fit the model
bigclam.fit(G)

# Get the embedding and memberships
embedding = bigclam.get_embedding()
memberships = bigclam.get_memberships()

# Display the results
print('Embedding:')
print(embedding)
print('\nMemberships:')
print(memberships)
<|EOT|>
