<｜begin▁of▁sentence｜>### Instruction:
Given a karate club graph with nodes representing the members and edges their mutual relations, with each node having a unique identifying feature, how can we generate a node embedding using the ASNE model from the TKDE ‘18 paper “Attributed Social Network Embedding”? Assume we want the embedding to be of 128 dimensions, utilize 4 worker processes during training, pass through the graph for 100 epochs, apply a down sampling frequency of 0.0001, a learning rate of 0.05, a minimum count of node occurrences of 1, and use a random seed of 42.
### Response:
import networkx as nx
from karateclub import ASNE
from scipy.sparse import coo_matrix
import numpy as np

# Create a graph
G = nx.karate_club_graph()

# Define the features as a COO formatted feature matrix
# Here we simply use an identity matrix for illustrative purposes
N = len(G)
features = coo_matrix(np.eye(N))

# Instantiate the ASNE model
model = ASNE(dimensions=128, workers=4, epochs=100, down_sampling=0.0001,
             learning_rate=0.05, min_count=1, seed=42)

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

# Get the embeddings
embeddings = model.get_embedding()
print(embeddings)
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