<｜begin▁of▁sentence｜>### Instruction:
As a fashion stylist, you're familiar with selecting and coordinating clothing and accessories for various fashion shoots or events. Today, you're exploring a new approach towards analyzing clothing trends using graph theory. Imagine you're working with a dataset that contains information about clothing items, their attributes, and their relationships with each other. You're interested in using the WaveletCharacteristic model from the CIKM ‘21 paper “Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization” to extract graph-level statistics and obtain their embeddings. This would enable you to capture the overall distribution and characteristics of the clothing items within the graph, providing valuable insights into trend analysis and fashion coordination.
### Response:
import networkx as nx
from karateclub import WaveletCharacteristic
import numpy as np

# Create a list of small graphs
G1 = nx.cycle_graph(5)
G2 = nx.path_graph(5)
graphs = [G1, G2]

# Initialize the WaveletCharacteristic model
wavelet_model = WaveletCharacteristic(order=5, eval_points=5, theta_max=2.5, tau=1.0, pooling='mean')

# Fit the model
wavelet_model.fit(graphs)

# Get the graph embeddings
embeddings = wavelet_model.get_embedding()

print(embeddings)
<|EOT|>
