<｜begin▁of▁sentence｜>### Instruction:
As a Leisure Travel Agent who specializes in planning bespoke travel experiences, imagine a scenario where you have been tasked with seamlessly orchestrating two distinct travel itineraries. The first involves organizing an adventurous cycling tour for a group of five avid cyclists looking to explore scenic routes together. The second itinerary entails curating a luxurious getaway for a group of six friends with a central focal point, such as a deluxe spa retreat or a private villa experience.

Utilizing your expertise in networking and coordination, consider the Graph2Vec model as a unique tool that may assist you in capturing the essence of each itinerary and translating it into comprehensive embedding representations. By leveraging this model, you aim to encapsulate the intrinsic characteristics and connections within these diverse travel plans, thereby enhancing the tailored experiences for your clients.
### Response:
import networkx as nx
from karateclub import Graph2Vec

# Creating two simple graph examples
G1 = nx.cycle_graph(5)
G2 = nx.star_graph(5)

# List of graphs
graphs = [G1, G2]

# Initialize Graph2Vec
model = Graph2Vec(wl_iterations=2, attributed=False, dimensions=128, workers=4,
                 down_sampling=0.0001, epochs=10, learning_rate=0.025,
                 min_count=5, seed=42, erase_base_features=False)

# Fit the model
model.fit(graphs)

# Get the graph embeddings
embeddings = model.get_embedding()

print('Embeddings:\n', embeddings)
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