<｜begin▁of▁sentence｜>### Instruction:
As a data scientist working at a research institution, you're tasked with analyzing collaboration networks within the academic community. Specifically, you're interested in the Condensed Matter Collaborations of 2005 and want to perform attributed community detection using the I-Louvain algorithm provided by the Community Discovery Library (cdlib). Each researcher in the network is assigned two attributes: 'subject', which can be 'Physics', 'Math', or 'Biology' at random, and 'degree', which corresponds to the researcher's degree in the collaboration network. After applying the detection algorithm, you need to save and print the JSON representation of the detected communities for further analysis.

Please ensure all communities are correctly attributed and represented in the JSON output.
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Load the graph
G = nx.read_gml('cond-mat-2005.gml')

# Define node attributes for I-Louvain
attributes = {node: {'subject': random.choice(['Physics', 'Math', 'Biology']), 'degree': G.degree(node)} for node in G.nodes()}

# Run I-Louvain algorithm
communities = algorithms.ilouvain(G, attributes)

# Save the resulting communities to JSON
communities_json = communities.to_json()
print(communities_json)
<|EOT|>
