<｜begin▁of▁sentence｜>### Instruction:
Given the graph of 'Les Miserables' from a file named 'lesmis.gml', can you use the Louvain and Label Propagation community detection algorithms in cdlib to perform community detection? Also, can you compute the Adjusted Normalized Mutual Information score of each method and generate a plot using the plot_scoring function?
### Response:
from cdlib import algorithms, viz, evaluation, NodeClustering
import networkx as nx
import matplotlib.pyplot as plt

# Load the Les Miserables graph
G = nx.read_gml('lesmis.gml')

# Perform community detection using Louvain and Label Propagation algorithms
louvain_communities = algorithms.louvain(G)
label_propagation_communities = algorithms.label_propagation(G)

# Store the resulting NodeClustering objects
communities = [louvain_communities, label_propagation_communities]

# Store method names for plotting
method_names = ['Louvain', 'Label Propagation']

# Use the Adjusted Normalized Mutual Information as scoring function
scoring = evaluation.adjusted_mutual_information

# Visualize the scoring
viz.plot_scoring([G], communities, ['Les Miserables'], method_names, scoring=scoring, nbRuns=1)

# Display the plot
plt.show()
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