<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, I aim to provide guidance on using the NMFADMM model to obtain node embeddings from an Erdos-Renyi graph with 100 nodes. This technique can be likened to studying fossils and prehistoric life forms to comprehend Earth's history, evolution, and ancient environments, much like a paleontologist would do. In this particular instance, we are looking at an Erdos-Renyi graph G(n=100, p=0.05) to delve into the embedding of the graph's nodes using Non-negative Matrix Factorization with ADMM, as outlined in the ICASSP '14 paper.
### Response:
import networkx as nx
from karateclub import NMFADMM

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

# Initialize NMFADMM model with default parameters
model = NMFADMM()

# Fit the model
model.fit(G)

# Get the embedding
embedding = model.get_embedding()

print(embedding)
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