<｜begin▁of▁sentence｜>### Instruction:
Imagine you're the choreographer for a production that showcases two different dance troupes: 'Troupe A' representing 'Category_1' and 'Troupe B' representing 'Category_2'. In total, there are 100 dancers, with an equal split of 50 in each troupe. You wish to visualize and compare the level of interaction between the dancers within each troupe. Each dancer's interaction frequency is akin to the concept of node degrees in a social network graph.

To create this visual representation, think of using the 'degreeplot' function, which is part of the 'graspologic.plot' API, as your stage. The goal is to present a comparative plot that highlights how often dancers of 'Troupe A' interact with one another versus the interactions within 'Troupe B'. Can you conceptualize how to set the 'degreeplot' stage for your performance analysis, focusing on illustrating the varying interaction levels across the two different troupes?
### Response:
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from graspologic.plot import degreeplot

# Create a synthetic graph
G = nx.erdos_renyi_graph(n=100, p=0.05)

# Convert the graph to an adjacency matrix
X = nx.to_numpy_array(G)

# Assume we have two categories of nodes which we represent using labels
labels = np.array(["Category_1" if i<50 else "Category_2" for i in range(100)])

# Plot the degree distribution
ax = degreeplot(X, labels=labels, direction='out', title='Degree Distribution', context='notebook', font_scale=1.25, figsize=(10, 6), palette='pastel')
plt.show()
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