<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, knowing a variety of graph structures is crucial for understanding and interpreting complex datasets. Suppose you have a set of small graphs comprising a complete graph, a path graph, and a circular ladder graph, each with 5 nodes. How can we leverage the WaveletCharacteristic model introduced in the CIKM '21 paper to create embeddings that capture the distinctive features of these graphs?
### Response:
import networkx as nx
from karateclub import WaveletCharacteristic
import numpy as np

# Create a list of 3 small graphs
g1 = nx.complete_graph(5)
g2 = nx.path_graph(5)
g3 = nx.circular_ladder_graph(5)
graphs = [g1, g2, g3]

# Initialize WaveletCharacteristic with specified hyperparameters
model = WaveletCharacteristic(order=5, eval_points=5, theta_max=2.5, tau=1.0, pooling='mean')

# Fit the model to the graphs
model.fit(graphs)

# Get the embedding of the graphs
embedding = model.get_embedding()

print(embedding)
<|EOT|>
