<｜begin▁of▁sentence｜>### Instruction:
Imagine you are working on a social media platform that aims to connect users with similar interests and hobbies. You want to analyze the network structure of the platform to understand how users are grouped into communities based on their interactions and connections. Using the GNP random graph with 100 nodes and a probability of edge creation of 0.05 as a model for the network, you would like to apply the MNMF model ('Community Preserving Network Embedding') to obtain embeddings, cluster centers, and cluster memberships of the users. This will help in visualizing the community distribution and enhancing the platform's recommendation system to suggest relevant connections to users.
### Response:
import networkx as nx
from karateclub import MNMF
import numpy as np

# Step 1: Create a graph
G = nx.gnp_random_graph(100, 0.05)

# Step 2: Apply the MNMF model
model = MNMF(dimensions=128, clusters=10, lambd=0.2, alpha=0.05, beta=0.05, iterations=200, lower_control=1e-15, eta=5.0, seed=42)
model.fit(G)

# Step 3: Get the cluster centers, embeddings, and memberships
cluster_centers = model.get_cluster_centers()
embeddings = model.get_embedding()
memberships = model.get_memberships()

print(cluster_centers)
print(embeddings)
print(memberships)
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