<｜begin▁of▁sentence｜>### Instruction:
As an event planner, you've been tasked with organizing a conference for a network science convention. To ensure better networking opportunities among participants, the organizers aim to create personalized connections by understanding the underlying relationships within a hypothetical 100-node network.

Using Node2Vec, a graph embedding technique, you need to generate embeddings based on a random graph with 100 nodes. In this graph, each pair of nodes has a 50% chance of being connected, replicating the spontaneity of professional interactions at the conference. The resulting embeddings will help identify potential networking clusters and bridge-building opportunities, adding value to the overall conference experience.
### Response:
import networkx as nx
from karateclub import Node2Vec

# Creating a random graph using NetworkX
G = nx.fast_gnp_random_graph(100, 0.5)

# Initializing Node2Vec model with custom parameters
model = Node2Vec(walk_number=10, walk_length=80, p=1, q=1, dimensions=64, workers=4, window_size=5, epochs=1, learning_rate=0.05, min_count=1, seed=42)

# Fitting the model
model.fit(G)

# Getting the embedding
embedding = model.get_embedding()

print(embedding)
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