<｜begin▁of▁sentence｜>### Instruction:
As a business analyst, you have been tasked with analyzing a collaboration network within the high-energy theory domain to identify potential areas for collaboration and community formation among researchers. The network data is available in a GML file named hep-th.gml, and it includes attributes such as 'energy' and 'theory' assigned to each node. Your goal is to utilize the I-Louvain algorithm in cdlib to perform attributed community detection, considering the node attributes of 'energy' and 'theory'. Once the communities are detected, you are expected to provide a JSON representation of the identified communities.
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Create a graph from the High-energy theory collaborations file
G = nx.read_gml('hep-th.gml')

# Define a function to assign random attributes to the nodes
def assign_random_attributes(graph, attr_name, possible_values):
    attrs = {node: {attr_name: random.choice(possible_values)} for node in graph.nodes()}
    nx.set_node_attributes(graph, attrs)

# Assign random attributes to nodes
assign_random_attributes(G, 'energy', [0.1, 0.4, 0.5])
assign_random_attributes(G, 'theory', [34, 3, 112])

# Retrieve the attributes for I-Louvain
labels = {node: G.nodes[node] for node in G.nodes()}

# Apply the I-Louvain algorithm for attributed community detection
communities = algorithms.ilouvain(G, labels)

# Get a JSON representation of the detected communities
communities_json = communities.to_json()

print(communities_json)
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