<｜begin▁of▁sentence｜>### Instruction:
As a UX Designer, you're tasked with enhancing the usability and satisfaction of digital platforms. Imagine you're working on a social networking app that connects users based on their interests within communities. You're considering implementing a network analysis tool, leveraging the Newman-Watts-Strogatz graph algorithm. 

Your goal is to use the MNMF model (Community Preserving Network Embedding) from AAAI ‘17 to generate node embeddings, cluster centers, and node cluster memberships. Specifically, you have a network of 100 users, each having connections to 10 others, and with a rewiring probability of 0.5. You need to tailor the dimensionality and clustering parameters to suit the needs of your app's community detection and recommendation system.

How would you integrate this functionality seamlessly into your app's interface to provide users with insightful community recommendations while maintaining a user-friendly experience?
### Response:
import networkx as nx
from karateclub import MNMF

# Create a synthetic graph
G = nx.newman_watts_strogatz_graph(100, 10, 0.5)

# Instantiate the MNMF model with custom parameters
model = MNMF(dimensions=64, clusters=5, lambd=0.2, alpha=0.05, beta=0.05, iterations=100, lower_control=1e-15, eta=5.0, seed=42)

# Fit the model on the synthetic graph
model.fit(G)

# Get the embeddings and memberships
embeddings = model.get_embedding()
cluster_centers = model.get_cluster_centers()
memberships = model.get_memberships()

# Output the results
print('Embeddings: ', embeddings)
print('Cluster Centers: ', cluster_centers)
print('Memberships: ', memberships)
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