<｜begin▁of▁sentence｜>### Instruction:
As a domain expert, you would like to effectively embed a given GNP random graph with 100 nodes and a probability of 0.05 for edge creation into a 64-dimensional vector space using an AE model. Each node needs to perform 10 different random walks of length 80, with a Skip-Gram window size of 5. The model should run for 3 epochs using 4 workers. Additionally, there is a binary feature matrix associated with the nodes, where each node has a single feature set to 1. This embedding strategy aims to capture the structural and feature information of the graph for further analysis and processing.
### Response:
import networkx as nx
from karateclub import AE
from scipy.sparse import coo_matrix
import numpy as np

# Create a graph
G = nx.gnp_random_graph(100, 0.05)

# Create a binary feature matrix
data = np.ones(100)
row = np.arange(100)
col = np.arange(100)
X = coo_matrix((data, (row, col)), shape=(100, 100))

# Initialize AE model
model = AE(walk_number=10, walk_length=80, dimensions=64, workers=4, window_size=5, epochs=3, learning_rate=0.01, down_sampling=0.0001, min_count=1, seed=42)

# Fit model
model.fit(G, X)

# Get embedding
embedding = model.get_embedding()

print(embedding)
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