<｜begin▁of▁sentence｜>### Instruction:
As a fashion stylist, I often need to analyze the latest fashion trends and determine the best combinations of clothing and accessories for my clients. Recently, I've been tasked with analyzing a large dataset of neural network graphs to identify the most influential connections within the network. I plan to use the Louvain algorithm available in the cdlib library to perform community detection on the C. elegans neural network graph, which I have accessed from a GML file named celegansneural.gml. Additionally, I aim to evaluate the quality of the detected community structure using the Surprise metric. This will allow me to provide valuable insights and make informed decisions about the fashion trends to follow.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

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

# Apply the Louvain algorithm for community detection
communities = algorithms.louvain(g)

# Compute the Surprise score to evaluate the quality of the community structure
eval_surprise = evaluation.surprise(g, communities)

# Print the Surprise score
print('Surprise score:', eval_surprise.score)
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