<｜begin▁of▁sentence｜>### Instruction:
As a jazz musician, let's imagine you're composing a piece for a grand performance at a renowned jazz festival. To infuse your composition with a dynamic structure, you've decided to draw inspiration from the interconnectedness of networks, much like the intricate relationships between jazz musicians on stage.

Now, envision you have a scenario where you're collaborating with a team of mathematicians and computer scientists who specialize in network analysis. They've provided you with a tool called NetMF (Network Matrix Factorization), reminiscent of how jazz musicians riff off each other's improvisations. This tool is inspired by a paper presented at the WSDM '18 conference.

Here's your scenario: You're working on a section of your composition that represents the vibrant interplay between 100 jazz musicians, each symbolizing a node in a musical network. Your mathematician colleagues have created a simulated musical network for you using a GNP (Gilbert-Newman) random graph with 100 nodes. In this simulation, the probability of a musical connection between any two musicians is set at 0.05, reflecting the spontaneity and unpredictability often found in jazz performances.

Now, your task is to employ the NetMF tool to extract node embeddings from this musical network. These embeddings will capture the essence of each musician's style, much like how a jazz musician's unique improvisational flair defines their musical identity. The NetMF model operates with specific parameters: it generates embeddings in 32 dimensions, iterates 10 times for Singular Value Decomposition (SVD), explores up to the second-order Pointwise Mutual Information (PMI) matrix powers, incorporates 1 negative sample for contrast, and uses a random seed value of 42 to ensure consistency in results. 

With these embeddings at your disposal, you aim to enrich your composition with nuanced musical motifs inspired by the intricate relationships within the jazz community.
### Response:
import networkx as nx
from karateclub import NetMF
import numpy as np

# Create a graph
G = nx.gnp_random_graph(100, 0.05)

# Initialize NetMF model with default parameters
default_model = NetMF()
# Fit the model to the graph
default_model.fit(G)

# Get the embedding of nodes
embedding = default_model.get_embedding()

print(embedding)
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