<｜begin▁of▁sentence｜>### Instruction:
In the realm of societal networks, imagine we've mapped the connections within a small community of 10 individuals, encapsulating their interactions in an adjacency matrix 'A'. Concurrently, we've classified these individuals into distinct subgroups based on certain demographic characteristics, with this information encapsulated in a dataframe titled 'meta'.

To effectively illustrate and interpret the underlying network structure, how might one utilize the `adjplot` function from the graspologic library? Specifically, we seek to produce a heatmap representation of the adjacency matrix 'A', arranging and delineating the nodes in a manner that reflects the subgroup affiliations as documented in 'meta'. This visual representation would serve as an invaluable tool for discerning the intricate tapestry of communal ties and the extent of inter- and intra-group connectivity.
### Response:
import numpy as np
import pandas as pd
import seaborn as sns
from graspologic.plot import adjplot

# Define a 10x10 adjacency matrix for a simple undirected graph
np.random.seed(42)
A = np.random.randint(0, 2, size=(10, 10))
A = np.triu(A) + np.triu(A).T  # Make the matrix symmetric (undirected graph)
np.fill_diagonal(A, 0)  # No self-loops

# Define metadata for the nodes
meta = pd.DataFrame({'community': ['A', 'A', 'B', 'B', 'C', 'C', 'A', 'A', 'B', 'C']})

# Plot the adjacency matrix with community information
ax = adjplot(data=A,
             plot_type='heatmap',
             meta=meta,
             group='community',
             group_order='size',
             palette='tab10',
             ticks=True)
sns.despine()

# Display the plot
plt.show()
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