<｜begin▁of▁sentence｜>### Instruction:
As a musician, imagine you're part of a band preparing for a gig at a local venue. You've been tasked with enhancing the band's online presence by incorporating network analysis into your promotional strategy. Your band's social network comprises 100 fans, each representing a node, and you're interested in how they're connected.

Utilizing the BoostNE model, akin to a sophisticated instrument in your musical toolkit, you aim to embed this social network data. By employing BoostNE's default settings—8 dimensions, 16 iterations, order 2, alpha 0.01, and seed 42—you intend to generate a nuanced representation of fan interactions. This will not only enrich your understanding of fan engagement dynamics but also inform targeted promotional efforts for your upcoming performances.
### Response:
import networkx as nx
from karateclub import BoostNE

# Step 1: Create a graph
G = nx.gnp_random_graph(100, 0.1)

# Step 2: Apply the BoostNE model with default 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)

# Step 3: Fit the model
boostne_model.fit(G)

# Step 4: Get the embeddings
embeddings = boostne_model.get_embedding()

print('Embeddings:\n', embeddings)
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