<｜begin▁of▁sentence｜>### Instruction:
As an astrobiologist, you're intrigued by the complexities of life beyond Earth and are always exploring new methods to understand its potential existence. In your research, you've obtained a fascinating dataset representing the interconnectedness of Internet Autonomous Systems, akin to the intricate web of relationships that may define life in distant corners of the universe.

Now, imagine you've come across a GML file named "as-22july06.gml," containing valuable insights into these interconnected systems. To unravel its secrets, you're keen on employing advanced network analysis techniques.

One method catching your interest is the Louvain algorithm, a powerful tool available in the Complex Network Library (cdlib). This algorithm can help identify distinct communities within the network, mirroring how life forms might organize in diverse environments.

Moreover, you're intrigued by the concept of internal edge density, akin to the density of interactions within a community. By computing this metric for the detected community structure, you aim to gain deeper insights into the cohesion and organization of these interconnected systems, much like deciphering the dynamics of ecosystems in the search for habitable environments beyond Earth.

In summary, your mission involves applying the Louvain algorithm from cdlib to detect communities within the Internet Autonomous Systems graph derived from "as-22july06.gml" and subsequently calculating the internal edge density to unravel the network's underlying structure, drawing parallels to the exploration of potential life in the vast expanse of the universe.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Create a graph from the provided Internet AS graph gml file
G = nx.read_gml('as-22july06.gml')

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

# Compute the internal edge density
internal_edge_dens = evaluation.internal_edge_density(G, communities)
print(internal_edge_dens.score)
<|EOT|>
