<｜begin▁of▁sentence｜>### Instruction:
As a Solar Energy Technician, you're tasked with ensuring the seamless installation and maintenance of solar energy systems for both residential and commercial purposes. Imagine you've been provided with a comprehensive blueprint of a solar energy network, represented as a graph in a file called "netscience.gml". Your goal is to optimize the efficiency of this network by identifying distinct communities within it, akin to clusters of interconnected solar panels. To achieve this, you'll employ the Louvain method available in the CDLib library. Additionally, you're required to compute the average embeddedness of nodes within each community, which essentially measures the interconnectedness of solar panels within these clusters. This analysis will not only enhance the understanding of the network's structure but also pave the way for strategic improvements in its functionality and performance.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

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

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

# Compute the average embeddedness of nodes within each community
avg_embeddedness = evaluation.avg_embeddedness(G, communities)

# Print the average embeddedness results
print(avg_embeddedness)
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