<｜begin▁of▁sentence｜>### Instruction:
Given the coauthorships in network science graph, which you can read from a GML file named netscience.gml, how can you compare the community structures detected by the Louvain and Walktrap community detection algorithms? More specifically, how can you visualize the similarity between the partitions obtained by these two algorithms using the Adjusted Mutual Information (AMI) score?
### 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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