<｜begin▁of▁sentence｜>### Instruction:
As a nonprofit administrator, you're tasked with optimizing community outreach efforts for a local organization. Let's say you're collaborating with a team of data scientists to enhance your understanding of community networks. They've gathered data from surveys conducted among 100 community members, capturing various connections between them. The team plans to utilize the GEMSEC model, a cutting-edge tool in network analysis, to delve into this data.

Now, the team aims to apply the GEMSEC model to this dataset, which represents a network of 100 individuals with edges created between them based on a probability of connection. This probability, akin to the likelihood of individuals interacting or being part of the same community events, is set at 0.05. The objective is twofold: first, to extract embeddings representing the individuals in the network, providing insights into their roles and relationships; and second, to identify clusters within the network, highlighting distinct community groups or affiliations.

In simpler terms, the team seeks to use advanced algorithms to uncover hidden patterns within the community network data, aiding in strategic decision-making and targeted outreach efforts.
### Response:
import networkx as nx
from karateclub import GEMSEC

# Create a random graph
G = nx.gnp_random_graph(n=100, p=0.05)

# Instantiate GEMSEC model
model = GEMSEC(walk_number=10, walk_length=80, dimensions=32, negative_samples=10, 
             window_size=10, learning_rate=0.05, clusters=5, gamma=0.1, seed=42)

# Fit the model
model.fit(G)

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

# Get the memberships
memberships = model.get_memberships()
print(memberships)
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