<｜begin▁of▁sentence｜>### Instruction:
As a molecular biologist delving into the intricacies of molecular interactions, imagine you're exploring a network of 100 molecules, each closely connected to three nearest neighbors, resembling the bonding patterns found in biological systems. Now, let's introduce a twist to our network: a rewiring probability of 0.5, simulating the possibility of molecular rearrangement.

To further analyze this network, let's employ the BoostNE model, a tool akin to a powerful microscope for molecular networks. We'll configure it with 16 dimensions, akin to different perspectives to scrutinize our molecules, and run it through 10 boosting iterations to refine our insights. Additionally, we'll set the order to 3, reflecting the depth of interactions we wish to explore.

To fine-tune our analysis, we'll set alpha to 0.05, akin to adjusting the focus of our microscope, and fix our random seed at 90 to ensure reproducibility in our observations.

With this setup, we aim to obtain node embeddings, akin to capturing snapshots of our molecules' positions in this dynamic network, shedding light on their roles and relationships within biological processes.
### Response:
import networkx as nx
from karateclub import BoostNE

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

# Initialize BoostNE
boostne_model = BoostNE(dimensions=16, iterations=10, order=3, alpha=0.05, seed=90)

# Fit the model
boostne_model.fit(G)

# Get the embedding
embedding = boostne_model.get_embedding()
print(embedding)
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