<｜begin▁of▁sentence｜>### Instruction:
As a Grant Coordinator, imagine you're working with a research team focused on analyzing social network dynamics. You've been tasked with enhancing the analysis of a specific network using cutting-edge techniques. 

Consider a scenario where the research team has gathered data on a social network representing interactions within a community. The network exhibits a star-like structure with a central figure connected to several peripheral members. To deepen the analysis, you're exploring the utilization of advanced algorithms to enhance the understanding of node relationships beyond traditional methods.

You're particularly interested in leveraging the NEU (Network Embedding with Unsupervised Learning) model to augment the embeddings generated by Node2Vec. The aim is to capture higher-order proximities within the network, which could reveal nuanced insights into community dynamics and influence patterns.

Your task involves integrating NEU functionalities into the existing analysis pipeline. This may entail leveraging APIs such as `neu-enhance()` to apply the NEU model to the Node2Vec embeddings, thereby enriching the representation of nodes within the network. Ultimately, this enhanced embedding will enable the research team to uncover deeper insights into the social dynamics and structural properties of the community network, supporting informed decision-making and further research endeavors.
### Response:
import networkx as nx
from karateclub import Node2Vec, NEU
import numpy as np

# Create a simple graph
G = nx.star_graph(4)

# Generate the initial embedding with Node2Vec
node2vec = Node2Vec()
node2vec.fit(G)
initial_embedding = node2vec.get_embedding()

# Apply NEU to enhance the initial embeddings
neu = NEU(L1=0.5, L2=0.25, T=1, seed=42)
neu.fit(np.array(initial_embedding))

# Get the enhanced embedding
enhanced_embedding = neu.get_embedding()
print(enhanced_embedding)
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