<｜begin▁of▁sentence｜>### Instruction:
As a pharmaceutical sales representative, you might be interested in understanding how a novel model called 'WaveletCharacteristic' can be applied to analyze the structural characteristics of different networks. Suppose you're discussing potential collaborations with healthcare institutions to improve supply chain efficiency or optimize patient care networks.

In this context, let's say you have two types of networks to analyze: one representing the supply chain structure of pharmacies (a star graph with 4 nodes), and the other representing patient referral patterns among hospitals (a cycle graph with 5 nodes).

You're curious about how the 'WaveletCharacteristic' model, as introduced in the CIKM ‘21 paper, can provide embeddings for these networks. These embeddings could potentially reveal important structural features that influence supply chain dynamics or healthcare network efficiency.

Could you provide insights on how to leverage this model to obtain embeddings for these specific graphs, and what information do these embeddings convey about the underlying network structures?
### Response:
import networkx as nx
from karateclub import WaveletCharacteristic
import numpy as np

# Create two small NetworkX graphs
G1 = nx.star_graph(4)
G2 = nx.cycle_graph(5)

# Instantiate the WaveletCharacteristic with specified parameters
model = WaveletCharacteristic(order=5, eval_points=5, theta_max=2.5, tau=1.0, pooling='mean')

# Fit the model on a list of graphs
model.fit([G1, G2])

# Get the embeddings for the list of graphs
embeddings = model.get_embedding()

print(embeddings)
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