<｜begin▁of▁sentence｜>### Instruction:
As a photo retoucher working on a project involving network analysis for a photography exhibition, you're tasked with enhancing the visualization of a network representing connections between 100 influential photographers. Your goal is to create visually appealing node embeddings using a specialized algorithm called GraphWave. 

You'll be using the GraphWave model, based on the techniques outlined in the KDD ‘18 paper “Learning Structural Node Embeddings Via Diffusion Wavelets”. To do this, you'll work with a dataset representing the connections between these photographers, where each connection has a probability of 0.05 of being present. 

Your specific parameters for generating these embeddings include:
- Sample number: 200
- Step size: 0.1
- Heat coefficient: 1.0
- Chebyshev polynomial approximation order: 100
- Approximate wavelet calculation method: Switch to approximation for vertex cardinalities beyond 1000
- Random seed value: 42

To implement this, you'll utilize the NetworkX library, incorporating its capabilities to apply the GraphWave algorithm to the network dataset. This will allow you to generate enhanced visual representations of the photographers' connections, providing deeper insights into their relationships and influences within the photography community.
### Response:
import networkx as nx
from karateclub import GraphWave

# Step 1: Create a graph
G = nx.gnp_random_graph(100, 0.05)

# Step 2: Initialize GraphWave with custom settings
model = GraphWave(sample_number=200, step_size=0.1, heat_coefficient=1.0,
                  approximation=100, mechanism='approximate', switch=1000, seed=42)

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

# Step 4: Extract the embedding
embedding = model.get_embedding()

print(embedding)
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