<｜begin▁of▁sentence｜>### Instruction:
As a Green Building Consultant, you have been tasked with providing guidance on implementing sustainable and environmentally friendly practices and materials for a construction project. The project involves a neural network graph of C. elegans, which has been read from a GML file named celegansneural.gml. The client is interested in using the 'Eva' function in the cdlib library to conduct attributed community detection, taking into account the type of neuron (sensory, interneuron, or motor) and signal (excitatory or inhibitory). Additionally, they require the resulting communities to be represented in a JSON format, with the labels for 'neuron_type' and 'signal_type' selected randomly for the example. How would you approach this scenario, ensuring that the attributed community detection is performed in accordance with the client's requirements?
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Load the C. elegans neural network graph
g = nx.read_gml('celegansneural.gml')

# Define the labels for the nodes based on a hypothetical label scheme
labels = {}
for node in g.nodes():
    labels[node] = {'neuron_type': random.choice(['sensory', 'interneuron', 'motor']),
                    'signal_type': random.choice(['excitatory', 'inhibitory'])}

# Apply the Eva algorithm to the graph
g_communities = algorithms.eva(g, labels, alpha=0.8)

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

print(communities_json)
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