<｜begin▁of▁sentence｜>### Instruction:
As a network engineer responsible for designing and managing computer networks, you have been tasked with analyzing a network of 100 nodes using an Erdos-Renyi model with a probability of edge creation set at 0.05. Your objective is to utilize the BoostNE model, based on the ASONAM '19 paper 'Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation', to generate node embeddings for the network. This will help in understanding the network's structure and relationships.
### Response:
import networkx as nx
from karateclub import BoostNE
import numpy as np

# Create a sample graph (e.g., Erdos-Renyi graph with 100 nodes, probability of edge creation is 0.05)
G = nx.erdos_renyi_graph(100, 0.05)

# Initialize BoostNE with the desired parameters
dimensions = 8
iterations = 16
order = 2
alpha = 0.01
seed = 42
boostne_model = BoostNE(dimensions=dimensions, iterations=iterations, order=order, alpha=alpha, seed=seed)

# Fit the model on the graph
boostne_model.fit(G)

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