<｜begin▁of▁sentence｜>### Instruction:
As a fraud investigator, you have a network of 100 financial transactions, and you are interested in analyzing the relationships between these transactions. Each transaction is associated with a set of 10 features that describe various attributes. To gain deeper insights into these relationships, you want to use the MUSAE model to transform the transaction network and its features into a 64-dimensional representation for each transaction. Can you outline the steps to achieve this using MUSAE with the networkx package?
### Response:
import networkx as nx
import numpy as np
from karateclub import MUSAE
from scipy.sparse import coo_matrix

# Creating a random graph with 100 nodes and a 0.05 probability of edge creation
G = nx.erdos_renyi_graph(100, 0.05)

# Creating a binary feature matrix
X = np.random.randint(2, size=(100, 10))
feature_matrix = coo_matrix(X)

# Initializing the MUSAE model with specified parameters
model = MUSAE(walk_number=10, walk_length=80, dimensions=64, workers=2, window_size=5, epochs=10, learning_rate=0.01, down_sampling=0.0001, min_count=1, seed=42)

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

# Retrieving the embeddings
embeddings = model.get_embedding()

# Output
print('Node embeddings:\n', embeddings)
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