<｜begin▁of▁sentence｜>### Instruction:
As a Project Manager, you are responsible for planning and overseeing projects to achieve specific goals within scope, schedule, budget, and quality constraints. Suppose you are leading a data analytics project for a sports organization where you have access to a graph representing the American College football network stored in a GML file named football.gml.

In order to optimize the network analysis and community detection within this football graph, you decide to leverage the Louvain algorithm available in the cdlib library. Your task is to utilize this algorithm to perform community detection on the football network graph and then compute the Surprise metric score to evaluate the quality of the detected communities. This metric will provide valuable insights into the structure and cohesion of the community partitions within the football graph, enabling you to make data-driven decisions for enhancing team dynamics and interaction patterns.

By leveraging the Louvain algorithm and the Surprise metric in the cdlib library, you can effectively analyze the American College football network to identify meaningful community structures and optimize team strategies based on the detected communities.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the American College football graph
G = nx.read_gml('football.gml')

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

# Compute the Surprise quality metric for the detected communities
surprise_metric = evaluation.surprise(G, communities)

print(surprise_metric.score)
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