<｜begin▁of▁sentence｜>### Instruction:
As an Information Architect, let's consider a scenario where you have a network of books about US politics, and the data for this network is stored in a GML file named polbooks.gml. Now, you need to use the Louvain method in the CDLIB API to perform community detection on this network. Additionally, you're interested in computing the conductance, which is the fraction of total edge volume that points outside the community. This will help in understanding the flow of information within and outside of the detected communities within the network.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Load the graph
G = nx.read_gml('polbooks.gml')

# Perform community detection
communities = algorithms.louvain(G)

# Compute the conductance
cond = evaluation.conductance(G, communities)

# Print the conductance
print(cond)
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