<｜begin▁of▁sentence｜>### Instruction:
As an adventure guide who specializes in leading outdoor activities and adventures for tourists, you may want to compare the routes and locations explored during two different excursions or adventures to evaluate their similarity. You can think of these routes as community structures within the larger network of possible adventure paths. Imagine you have two sets of GPS tracks from different excursions, representing the coauthorships in network science. You want to compare the similarity of the paths by using a tool to visualize the overlap and alignment of the paths. It's like comparing how two different adventure routes intersect and align with each other. The similarity can be quantified using the Adjusted Mutual Information (AMI) score, which will give you a measure of how closely the paths or community structures obtained by different algorithms align with each other.
### Response:
import networkx as nx
from cdlib import algorithms, viz, evaluation

# Load the coauthorships network science graph
g = nx.read_gml('netscience.gml')

# Detect communities using Louvain and Walktrap algorithms
coms_louvain = algorithms.louvain(g)
coms_walktrap = algorithms.walktrap(g)

# Plot similarity matrix using Adjusted Mutual Information (AMI) as the scoring function
clustermap = viz.plot_sim_matrix([coms_louvain, coms_walktrap], evaluation.adjusted_mutual_information)
print('Similarity matrix plotted')

# Assuming you run this in an environment that supports plotting, e.g., Jupyter Notebook
# If running in a script you can save the plot using
# clustermap.savefig('similarity_matrix.png')
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