<｜begin▁of▁sentence｜>### Instruction:
Given a Newman-Watts-Strogatz graph with 100 nodes, each connected to 3 nearest neighbors, and with a 0.05 probability of adding a new edge, can you use the BigClam model to determine the node-cluster affiliations and embeddings as described in the WSDM ‘13 paper 'Overlapping Community Detection at Scale: A Non-negative Matrix Factorization Approach'?
### 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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