<｜begin▁of▁sentence｜>### Instruction:
Given a collection of Erdős-Rényi graphs with n=10 nodes and p=0.5 probability of edge creation, how can one use the WaveletCharacteristic model (from CIKM ‘21 paper “Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization”) to embed these graphs into a vector space? Provide a Python code snippet for generating embeddings for three such graphs and describe how 'order', 'eval_points', 'theta_max', 'tau', and 'pooling' parameters of the model can be adjusted.
### Response:
import networkx as nx
from karateclub import WaveletCharacteristic
import numpy as np

# Step 1: Create an array of networkx graphs
graphs = [nx.gnp_random_graph(10, 0.5) for _ in range(3)]

# Step 2: Initialize the WaveletCharacteristic model with the given parameters
model = WaveletCharacteristic(order=5, eval_points=5, theta_max=2.5, tau=1.0, pooling='mean')

# Step 3: Fit the model to the graphs
model.fit(graphs)

# Step 4: Get the embedding of the graphs
embeddings = model.get_embedding()

print(embeddings)
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