<｜begin▁of▁sentence｜>### Instruction:
As a blockchain developer, you may encounter a scenario where you need to develop and implement a blockchain-based solution for secure data management in a small network. For example, consider a network representing a supply chain, with different nodes representing different entities involved in the supply chain, such as manufacturers, distributors, and retailers. The edges between nodes represent the flow of products or information. Furthermore, you have a feature matrix X containing important attributes of each node, such as production capacity, inventory levels, or transaction history.

To address this scenario, you may need to use the FSCNMF (Feature Selection and Clustering-based Non-negative Matrix Factorization) model to extract node embeddings from the network. These embeddings will capture the structural and feature-based representations of each node in the network, enabling better analysis and decision-making.

In order to achieve this, you would need to utilize networkx, a Python library for the creation, manipulation, and study of complex networks. Additionally, given the sparse COO matrix representing the feature matrix X, you can apply FSCNMF using respective Python packages such as scikit-learn or NIMFA for the matrix factorization process.

By leveraging these tools and techniques, you can optimize the management and decision-making processes within the supply chain network, ultimately enhancing security and operational efficiency.
### Response:
import networkx as nx
from karateclub import FSCNMF
from scipy.sparse import coo_matrix

# Sample graph
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0)])

# Sample feature matrix
data = [1, 2, 3, 4]
row = [0, 1, 2, 3]
col = [0, 1, 2, 3]
X = coo_matrix((data, (row, col)), shape=(4, 4))

# Instantiate and fit the model
dimensions = 16
model = FSCNMF(dimensions=dimensions, lower_control=1e-15, iterations=500, alpha_1=1000.0, alpha_2=1.0, alpha_3=1.0, beta_1=1000.0, beta_2=1.0, beta_3=1.0, seed=42)

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

# Get the embedding
embedding = model.get_embedding()
print('Embedding:\n', embedding)
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