<｜begin▁of▁sentence｜>### Instruction:
As a network analysis expert familiar with networkx, I am well-equipped to assist with your question regarding community structures in a graph of US politics books. Let's consider a scenario where you are a political science researcher studying the relationships between different books focusing on US politics. You have access to a dataset saved in a GML file named 'polbooks.gml', which represents the connections between these books.

Now, you are interested in comparing the community structures detected by the Louvain and Walktrap algorithms in this book network. By utilizing the adjusted mutual information scoring function, you aim to assess the similarity between the communities identified by these two algorithms. Additionally, you would like to visualize the similarity matrix to gain insights into the degree of agreement between the community structures detected by Louvain and Walktrap.

With your expertise in network analysis and the dataset at hand, you can delve into this comparative analysis to deepen your understanding of the underlying connections and groupings within the US politics books network.
### 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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