<｜begin▁of▁sentence｜>### Instruction:
As a Licensed Professional Counselor, imagine you're working with a community center that hosts various social events. You've been tasked with understanding the social dynamics within the community by analyzing interactions among attendees. 

Your task involves utilizing a tool called GEMSEC (Graph Embedding with Self Clustering) to gain insights from the data collected during these events. Specifically, you'll be focusing on a network of 100 individuals who attend these events regularly. This network is akin to a web of connections between individuals, where the probability of any two individuals forming a connection is 0.05.

To accomplish this, you'll use GEMSEC to transform the complex network of interactions into a simplified representation, extracting embeddings (or numerical representations) of each individual within the community. These embeddings capture the essence of each person's social connections and interactions.

Furthermore, GEMSEC not only provides embeddings but also clusters individuals based on their social similarities. This clustering helps identify groups or communities within the larger social network, shedding light on distinct social circles or cliques that may exist among attendees.

By employing GEMSEC, you aim to uncover patterns of social cohesion, identify key influencers within the community, and ultimately provide insights that can inform strategies for fostering a more connected and supportive social environment within the community center.
### Response:
import networkx as nx
from karateclub import GEMSEC

# Creating a random graph using networkx
G = nx.gnp_random_graph(100, 0.05)

# Initialize the GEMSEC model with specified hyperparameters
model = GEMSEC(walk_number=10, walk_length=80, dimensions=64, negative_samples=10, window_size=5, learning_rate=0.05, clusters=5, gamma=0.1, seed=42)

# Fitting the GEMSEC model on the graph G
model.fit(G)

# Getting the node embedding after the model has been fit
dimensional_embeddings = model.get_embedding()

# Getting the cluster membership of nodes
cluster_memberships = model.get_memberships()

print(dimensional_embeddings)
print(cluster_memberships)
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