<｜begin▁of▁sentence｜>### Instruction:
The photography studio is working on a collaborative project with a social research team. They have a concept for a photo exhibit that maps out the social dynamics within a karate club, represented through a network graph. To bring this idea to life, they plan to use the graspologic `layout_umap` function to create a 2D representation of the karate club network, showcasing connections and community clusters.

They want the exhibit to have both close-up shots focusing on individual relationships (akin to the local structure influence), and wide-angle shots that show the entire social scene (similar to the global structure influence). For these network perspectives, how can the photographers utilize the `min_dist` and `n_neighbors` parameters within the `layout_umap` function to adjust the level of detail and neighborhood consideration in their visual representation of the karate club network? 

Your question doesn't ask for an answer or a solution, but asks how to rephrase the initial question in this specific context.
### Response:
import networkx as nx
from graspologic.layouts import layout_umap
from matplotlib import pyplot as plt

# Create a simple graph
G = nx.karate_club_graph()

# Apply UMAP layout
layout_result = layout_umap(graph=G, min_dist=0.8, n_neighbors=10, random_seed=42)

# Extract node positions
positions = {node: (pos.x, pos.y) for node, pos in layout_result[1]}

# Draw the graph
nx.draw(G, pos=positions, node_size=50, with_labels=True)
plt.show()
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