<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, you may want to apply the SymmNMF algorithm from the SDM’12 paper “Symmetric Nonnegative Matrix Factorization for Graph Clustering” to a specific network, such as a social network of a local community. Using this algorithm, you can gain insights into how different groups or communities interact within the larger network. If you have access to the karate club's social network data, you could apply the SymmNMF model to derive the graph clustering and community memberships.
### Response:
import networkx as nx
from karateclub import SymmNMF

# Step 1: Create a graph
G = nx.karate_club_graph()

# Step 2: Apply the SymmNMF model
model = SymmNMF(dimensions=32, iterations=200, rho=100.0, seed=42)
model.fit(G)

# Step 3: Get embedding and memberships
embedding = model.get_embedding()
memberships = model.get_memberships()

# Outputs
print(embedding)
print(memberships)
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