<｜begin▁of▁sentence｜>### Instruction:
As a technical writer collaborating closely with data scientists and software engineers, you may encounter scenarios where you need to document the process of generating node embeddings in a network analysis task using the FSCNMF model within the context of a specific graph structure.

Consider a practical scenario where you're tasked with explaining how to derive node embeddings in a social network analysis project. Imagine you're documenting a feature within a software tool like NetworkX, a popular Python library for network analysis. Let's say you're working with a Newman-Watts-Strogatz small-world graph comprising 100 nodes, with each node initially connected to its three nearest neighbors and with a 50% probability of adding new edges.

In this scenario, you're aiming to employ the Feature-Subspace Compressive Non-negative Matrix Factorization (FSCNMF) model to obtain meaningful embeddings for the nodes in the graph. To illustrate this process effectively, you'd outline steps such as initializing the graph structure using NetworkX, setting up the parameters for the Newman-Watts-Strogatz model, and incorporating the identity matrix as the feature matrix representing each node's attributes.

Your documentation would need to articulate how the FSCNMF model operates within this context, detailing its role in generating embeddings that capture the underlying structure and relationships within the graph. You'd also emphasize the importance of understanding the parameters and nuances of both the graph generation process and the FSCNMF algorithm to ensure accurate and meaningful results.

Throughout your documentation, you'd strive to communicate complex technical concepts in a clear, concise manner, catering to a diverse audience ranging from data scientists and software developers to stakeholders and decision-makers. By leveraging technical writing principles and collaborating closely with subject matter experts, you'd aim to create comprehensive documentation that empowers users to effectively utilize the FSCNMF model for node embedding tasks in their network analysis projects.
### Response:
import networkx as nx
import numpy as np
from karateclub import FSCNMF
from scipy.sparse import coo_matrix

# Creating a graph
G = nx.newman_watts_strogatz_graph(100, 3, 0.5)

# Creating node features
features = np.identity(100)
# Converting numpy array to COO format
feature_matrix = coo_matrix(features)

# Initializing the FSCNMF model with default parameters
model = FSCNMF()

# Fitting the model with the graph and the feature matrix
model.fit(G, feature_matrix)

# Getting the embedding of nodes
embedding = model.get_embedding()

print(embedding)
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