<｜begin▁of▁sentence｜>### Instruction:
As a Network Analysis Specialist, when working on a project involving a simulation of sound propagation in a concert venue, you've utilized the Erdős-Rényi model to represent the network connectivity between 10 audio sources within the space with a probability of 0.5 for each potential audio connection. To effectively evaluate the cohesive block structure within the network and determine the hierarchy of interconnected audio sources, as well as identify the maximum cohesion for a particular audio source, you can leverage networkx, a Python package specifically designed for creating, manipulating, and studying complex networks. 

Could you provide me with additional details about the audio sources' positions and their connections to augment our analysis?
### Response:
from igraph import Graph

# Create a sample igraph graph
g = Graph.Erdos_Renyi(n=10, p=0.5, directed=False)

# Compute the cohesive blocks of the graph
cohesive_blocks = g.cohesive_blocks()

# Output the cohesive block structure
for i, block in enumerate(cohesive_blocks):
    print('Block #{}: {}'.format(i+1, block))
    print('Cohesion Level:', cohesive_blocks.cohesion(i))

# Get the graph that describes the hierarchy of blocks
hierarchy_graph = cohesive_blocks.hierarchy()

# Find the maximum cohesion for a given vertex
vertex_id = 5
max_cohesion = cohesive_blocks.max_cohesion(vertex_id)
print('Maximum cohesion of vertex {}: {}'.format(vertex_id, max_cohesion))
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