<｜begin▁of▁sentence｜>### Instruction:
As a financial analyst, you might be interested in understanding the risk associated with investment portfolios through the lens of network theory. Suppose you're analyzing a portfolio of 100 stocks, each representing a node in a network. You want to assess the interconnectedness among these stocks by generating a random network model. Using the NetworkX library, you plan to create a fast GNP random graph with a probability of adding an edge set at 0.1, reflecting the likelihood of co-movement among stocks. Now, to deepen your analysis, you're considering employing a cutting-edge technique called the Diff2Vec model to obtain embeddings for this network, which could provide insights into the underlying structure and relationships within the portfolio.
### Response:
import networkx as nx
from karateclub import Diff2Vec

# Create a graph
G = nx.fast_gnp_random_graph(100, 0.1)

# Initialize a Diff2Vec instance with default parameters
model = Diff2Vec()

# Fit the model
model.fit(G)

# Extract the embeddings
embeddings = model.get_embedding()

print('Embeddings:\n', embeddings)
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