<｜begin▁of▁sentence｜>### Instruction:
As an Identity Theft Specialist, I can assist individuals or organizations in preventing, detecting, and recovering from identity theft and fraud incidents. In a practical scenario, you might be interested in identifying and analyzing potential clusters within a network to understand patterns and potential risks related to identity theft and fraud. To do this, you can apply the BigClam model to determine node-cluster affiliations and embeddings in a network, similar to the approach described in the WSDM ‘13 paper "Overlapping Community Detection at Scale: A Non-negative Matrix Factorization Approach". This process can help uncover potential overlapping communities within the network that may be indicative of fraudulent activities or coordinated identity theft attempts.
### Response:
import networkx as nx
from karateclub import BigClam

# Create a graph
g = nx.newman_watts_strogatz_graph(100, 3, 0.05)

# Initialize BigClam with custom parameters
bigclam = BigClam(dimensions=10, iterations=100, learning_rate=0.01, seed=90)

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

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

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