<｜begin▁of▁sentence｜>### Instruction:
As a computer systems analyst, imagine you are working with a team to optimize the performance of a complex network of three interconnected systems. Each system is represented by a graph with 50 key components, and each interconnection has a 5% probability of impacting the overall network. To enhance our understanding of these graphs, you are asked to apply the 'SF' algorithm from the NeurIPS Relational Representation Learning Workshop ‘18 paper “A Simple Baseline Algorithm for Graph Classification” to create embeddings for each graph. These embeddings will provide insight into the structural characteristics and relationships within each system. Once obtained, the embeddings will be analyzed to identify patterns and develop strategies for enhancing the overall efficiency and effectiveness of the interconnected systems.
### Response:
import networkx as nx
from karateclub import SF
import numpy as np

# Create example graphs
G1 = nx.erdos_renyi_graph(n=50, p=0.05)
G2 = nx.erdos_renyi_graph(n=50, p=0.05)
G3 = nx.erdos_renyi_graph(n=50, p=0.05)
graphs = [G1, G2, G3]

# Initialize SF model with desired dimensions and seed
model = SF(dimensions=128, seed=42)

# Fit the model on the list of graphs
tions=128, seed=42).
model.fit(graphs)

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

print(embedding)
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