<｜begin▁of▁sentence｜>### Instruction:
As a medical researcher, your goal is to improve patient care by exploring new methods to enhance treatment outcomes. You have been studying a group of 100 patients with a particular medical condition, each exhibiting different symptoms and responses to treatment. You have collected extensive data on their conditions and responses to various interventions.

Now, you're interested in applying a cutting-edge technique called the Network Embedding with Higher-Order Proximities model to enhance your understanding of the relationships between the patients' conditions based on their responses to treatments. This approach involves using an initial random embedding of 128-dimensional vectors for each patient and generating a graph representation of the patient data using the Newman-Watts-Strogatz small-world graph model.

In this scenario, the "NEU model" corresponds to the specific method for enhancing the patient data embeddings, and its integration into the medical research domain aims to uncover higher-order proximities among patients' medical conditions and treatment responses.

By applying the NEU model to this patient data, you hope to gain deeper insights into the relationships between different medical conditions and treatment outcomes, ultimately aiming to improve the quality of care and the overall health of your patients.
### Response:
import networkx as nx
from karateclub import NEU
import numpy as np

# Create a graph
G = nx.newman_watts_strogatz_graph(100, 3, 0.5)

# Initial arbitrary embedding (e.g., using random vectors)
initial_embedding = np.random.rand(100, 128)

# Apply NEU model to the graph using the initial embeddings
neu_model = NEU(L1=0.5, L2=0.25, T=1, seed=42)
neu_model.fit(G, initial_embedding)

# Get the enhanced embedding
enhanced_embedding = neu_model.get_embedding()

print(enhanced_embedding)
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