<｜begin▁of▁sentence｜>### Instruction:
Given a random graph G with 100 nodes and 500 edges, can you use Diff2Vec model to get its embedding? The Diff2Vec model is from the CompleNet ‘18 paper “Diff2Vec: Fast Sequence Based Embedding with Diffusion Graphs” and is designed to create node embeddings based on diffusion processes within the graph.
### Response:
import networkx as nx
from karateclub import Diff2Vec

G = nx.gnm_random_graph(100, 500)

model = Diff2Vec(diffusion_number=10, diffusion_cover=80, dimensions=128, workers=4, window_size=5, epochs=1, learning_rate=0.05, min_count=1, seed=42)

model.fit(G)

embedding = model.get_embedding()
print(embedding)
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