<｜begin▁of▁sentence｜>### Instruction:
As a yoga instructor leading classes and sessions to promote physical, mental, and spiritual well-being, imagine you're tasked with analyzing the interconnectedness of three different types of yoga retreat centers, each catering to a unique community of practitioners. 

You have data on these centers, each represented by a network graph with 10 nodes. One center embodies the structure of a traditional yoga circle, fostering a cyclic flow of energy among its members. Another reflects the ethos of a small-world community, where practitioners are closely connected while still maintaining some degree of randomness in their interactions. The third center thrives on the principle of preferential attachment, where newer members are drawn to established ones, forming a network that mirrors the growth of a vibrant yoga community.

To gain deeper insights into the dynamics of these centers, you've decided to apply the NetLSD model, a tool akin to a yogic meditation practice, which helps reveal the subtle similarities and differences in the essence of these networks. By calculating their embeddings through NetLSD, you aim to understand the underlying structure of each community and how it influences the well-being of its members.
### Response:
import networkx as nx
from karateclub import NetLSD
import matplotlib.pyplot as plt

# Create example graphs
G1 = nx.cycle_graph(10)
G2 = nx.watts_strogatz_graph(10, 3, 0.1)
G3 = nx.barabasi_albert_graph(10, 2)

# List of graphs
graphs = [G1, G2, G3]

# Initialize NetLSD with default parameters
netlsd = NetLSD()

# Fit the model on the list of graphs	netlsd.fit(graphs)

# Get the embeddings
embeddings = netlsd.get_embedding()

print(embeddings)
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