<｜begin▁of▁sentence｜>### Instruction:
As a furniture maker, imagine you're designing a modular shelving system with 100 units, each representing a node in a network. You've arranged them in a grid-like structure where each unit is directly connected to its four nearest neighbors, forming a classic ring topology. However, to foster some serendipitous connections, you decide to introduce a touch of randomness. With a probability of 0.05, you add new connections between nodes, potentially creating shortcuts between distant units.

Now, imagine you're using a specialized tool called BoostNE, akin to a chisel in woodworking, to carve out unique patterns in the structure of this shelving system. Instead of wood, you're sculpting the network itself to extract meaningful representations of each unit, capturing their relationships and connections. The output of this process is a set of embeddings, like intricate engravings, that encode the essence of each node's position and influence within the network. These embeddings serve as a blueprint, guiding you in understanding the dynamics and interactions within your modular shelving system.
### Response:
import networkx as nx
from karateclub import BoostNE
import numpy as np

# Create a graph
G = nx.newman_watts_strogatz_graph(n=100, k=4, p=0.05, seed=42)

# Initialize BoostNE with specified parameters
model = BoostNE(dimensions=16, iterations=10, order=3, alpha=0.05, seed=42)

# Fit the model
model.fit(G)

# Get the embeddings
embeddings = model.get_embedding()

print('Node embeddings:\n', embeddings)
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