<｜begin▁of▁sentence｜>### Instruction:
As a Retail Buyer, you're tasked with selecting and purchasing merchandise for retail stores or online platforms. In this scenario, imagine you're working with a network of suppliers and need to optimize your purchasing decisions. You have a dataset representing relationships between different suppliers and their unique features, akin to how products might differ in quality, price, or delivery time. Now, utilizing the TADW (Topology Adaptive Deep Walk) framework, you aim to generate embeddings for each supplier node. These embeddings will capture the essence of each supplier's characteristics, helping you make informed decisions on which suppliers to engage with for various merchandise categories.
### Response:
import networkx as nx
from karateclub import TADW
import numpy as np
from scipy.sparse import coo_matrix

# Create a graph and initialize node feature matrix
G = nx.karate_club_graph()
features = np.identity(G.number_of_nodes())
feature_matrix = coo_matrix(features)

# Initialize TADW model with specific parameters
model = TADW(dimensions=32, reduction_dimensions=64, svd_iterations=20, seed=42, alpha=0.01, iterations=10, lambd=10.0)

# Fit the model
model.fit(G, feature_matrix)

# Get the node embeddings
embedding = model.get_embedding()

print(embedding)
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