<｜begin▁of▁sentence｜>### Instruction:
As an environmental scientist studying the impact of pollution on water ecosystems, I need to analyze two sets of data from different regions. Each dataset contains information on 100 distinct water bodies and the likelihood of pollution spreading between them, represented as a probability of 0.1 for edge creation between any two water bodies. To analyze and compare these datasets, I aim to use the 'SF' model to obtain a structured embedding based on the k lowest eigenvalues of their normalized Laplacian. If there are fewer eigenvalues than k, I will pad the results with zeros. This will help me understand the latent structure and connectivity within these water ecosystems, which is crucial for managing environmental impacts and risks.
### Response:
import networkx as nx
from karateclub import SF

# Create two graphs
G1 = nx.gnp_random_graph(100, 0.1)
G2 = nx.gnp_random_graph(100, 0.1)
graphs = [G1, G2]

# Initialize the SF model with the desired number of dimensions (eigenvalues)
model = SF(dimensions=128, seed=42)

# Fit the model to the graphs
model.fit(graphs)

# Get the embedding of the graphs
embedding = model.get_embedding()

print(embedding)
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