<｜begin▁of▁sentence｜>### Instruction:
As a network administrator, you have been tasked with analyzing the connectivity and clustering patterns in a newly deployed network with 100 nodes. To accomplish this, you need to create a 100-node random graph using the GNP random graph model with an edge probability of 0.05. Then, you must use the GEMSEC model to embed the nodes and determine their cluster memberships. Specifically, you should perform 10 random walks of length 40 and generate a 16-dimensional embedding using 10 negative samples and a window size of 3. Use a learning rate of 0.05 and set the random seed to 24.
Furthermore, the GEMSEC model should be configured to form 5 clusters with a clustering cost weight coefficient of 0.2. Once the embeddings and node cluster memberships are obtained, you need to visualize and analyze the results to gain insights into the network's structure and clustering tendencies.
### Response:
import networkx as nx
from karateclub import GEMSEC

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

# Initialize GEMSEC model with custom parameters
model = GEMSEC(walk_number=10, 
              walk_length=40, 
              dimensions=16, 
              negative_samples=10, 
              window_size=3, 
              learning_rate=0.05, 
              clusters=5, 
              gamma=0.2, 
              seed=24)

# Fit the model
model.fit(G)

# Get the embeddings and memberships
embeddings = model.get_embedding()
memberships = model.get_memberships()

# Print results
print('Embeddings:\n', embeddings)
print('Memberships:', memberships)
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