<｜begin▁of▁sentence｜>### Instruction:
Given a list of 10 randomly generated graphs with 100 nodes and a 0.1 probability of edge creation, how can you use the LDP (Local Degree Profile) model to generate an embedding for each graph that can be used for non-attributed graph classification?
### Response:
import networkx as nx
from karateclub import LDP
import matplotlib.pyplot as plt

# Create a list of graphs to be embedded
graph_list = [nx.gnp_random_graph(100, 0.1) for _ in range(10)]

# Initialize LDP model
ldp_model = LDP()

# Fit the LDP model
ldp_model.fit(graph_list)

# Get the embedding
embedding = ldp_model.get_embedding()

# Display the embedding
print(embedding)
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