<｜begin▁of▁sentence｜>### Instruction:
In the context of understanding social structures within a community network, such as the interactions observed in the well-studied Karate Club graph, we may be interested in generating a thermal representation to exhibit the affiliative patterns among the initial 10 members. This could emulate a common task in conservation biology where we often categorize species or habitats based on certain characteristics to understand their relationships and interactions.

To fulfill this goal, we can utilize the graspologic library – an insightful tool for network analysis similar to the way we use spatial analysis for habitat mapping. We intend to cluster these 10 individuals into a pair of distinct groups, possibly representing different social roles or ranks within the community. In our visual output, the aim is to maintain a focus purely on the interaction intensities by omitting any axis labels that may distract from the raw relational data.

Can we adapt this approach to metaphorically reflect how we might categorize a subset of a biological network, focusing solely on the connectivity heatmap, while leaving out the potentially extraneous details such as tick labels on both the x and y axes?
### Response:
import numpy as np
import networkx as nx
from graspologic.plot import heatmap

# Step 1: Create a graph
G = nx.karate_club_graph()
adjacency_matrix = nx.to_numpy_array(G)

# Step 2: Create hierarchical labels for the plot
inner_hier_labels = ['group1'] * 5 + ['group2'] * 5

# Step 3: Plot the heatmap of the graph's adjacency matrix
tax = heatmap(X=adjacency_matrix, inner_hier_labels=inner_hier_labels,
        title='Karate Club Adjacency Matrix', figsize=(10, 10), cmap='viridis',
        xticklabels=False, yticklabels=False, cbar=True)

# Show the plot
import matplotlib.pyplot as plt
plt.show()
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