<｜begin▁of▁sentence｜>### Instruction:
Given the Power grid Graph which you can read from a GML file named power.gml, can you use the Eva community detection algorithm from the cdlib library to perform attributed community detection, considering the type of each node as an attribute (Generator, Consumer, Transmitter)? The algorithm should account for both structural and attribute similarities between nodes, with an alpha parameter set to 0.5. Can you then generate and print the JSON representation of the resulting community structure?
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Read the power grid graph
G = nx.read_gml('power.gml')

# Define random attributes for each node
labels = {}
for node in G.nodes():
    labels[node] = {'Type': random.choice(['Generator', 'Consumer', 'Transmitter'])}

# Apply Eva community detection algorithm
communities = algorithms.eva(G, labels, alpha=0.5)

# Generate a JSON representation of the algorithm's object
json_representation = communities.to_json()
print(json_representation)
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