<｜begin▁of▁sentence｜>### Instruction:
As a paleoclimatologist, you're working on a research project to analyze the historical changes in environmental conditions for different regions. You have collected complex data on past climate patterns, and now you're interested in applying advanced community detection techniques to identify groups of regions with similar characteristics. Specifically, you are looking into using the I-Louvain algorithm to perform attributed community detection on a network of regions, each assigned a random attribute from a set of three possible values. After running the algorithm, your aim is to generate and print a JSON representation of the resulting attributed node clustering to better understand the distinct environmental patterns across different regions.
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Load the graph
G = nx.read_gml('football.gml')

# Create a dictionary to store the attributes
labels = {}
for node in G.nodes():
    labels[node] = {'attr': random.choice(['a', 'b', 'c'])}

# Apply the I-Louvain algorithm to the graph
communities = algorithms.ilouvain(G, labels)

# Generate JSON representation
communities_json = communities.to_json()
print(communities_json)
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