<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, you may be working on understanding and representing relationships within a large dataset. For instance, imagine you are tasked with analyzing a dataset representing communication structures within a large organization. You're interested in applying the Diff2Vec model to obtain node embeddings and capture the nuances of these communication patterns. The dataset consists of 100 individuals, and you expect there to be a 50% chance of communication occurring between any pair of individuals. In this scenario, implementing the Diff2Vec model with the specified hyperparameters will help you gain insights into the underlying structure of the communication network.
### Response:
import networkx as nx
from karateclub import Diff2Vec

# Create a graph
g = nx.fast_gnp_random_graph(100, 0.5)

# Initialize Diff2Vec
d2v = Diff2Vec(diffusion_number=10, diffusion_cover=80, dimensions=128, workers=4, window_size=5, epochs=1, learning_rate=0.05, min_count=1, seed=42)

# Fit the model
d2v.fit(g)

# Get the embedding
embedding = d2v.get_embedding()
print(embedding)
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