<｜begin▁of▁sentence｜>### Instruction:
Given the graph of books about US politics, which you can read from a GML file named 'polbooks.gml', can you compare the community structures detected by the Louvain and Walktrap community detection algorithms using the adjusted mutual information scoring function, and visualize their similarity matrix?
### Response:
import networkx as nx
from cdlib import algorithms, viz, evaluation

# Load a graph
G = nx.read_gml('polbooks.gml')

# Detect communities using two different algorithms
coms_louvain = algorithms.louvain(G)
coms_walktrap = algorithms.walktrap(G)

# Plot the similarity matrix using adjusted mutual information as the scoring function
clustermap = viz.plot_sim_matrix([coms_louvain, coms_walktrap], evaluation.adjusted_mutual_information)

# Display the plot
clustermap.plot()
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