<｜begin▁of▁sentence｜>### Instruction:
As a judge presiding over a criminal case involving multiple individuals, it is crucial to understand the cohesive block structure within the network of the defendants and their connections. Utilizing network analysis, we can represent the relationships among the individuals using a graph, with each vertex representing a defendant and the edges indicating their connections, such as co-conspirator relationships or shared criminal activities.

For instance, consider a scenario where the defendants are represented by vertices {0, 1, 2, 3, 4, 5, 6, 7, 8}, and their connections are defined by the following edges: [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (5, 6), (5, 7), (6, 7), (7, 8)]. In order to analyze the network structure, the cohesive_blocks function from the igraph API can be employed to determine the cohesive blocks present within the network, and further identify the blocks and the hierarchical levels that each defendant belongs to. This analysis would provide valuable insights into the groupings and affiliations among the defendants, aiding in the administration of justice and resolution of disputes.
### Response:
from igraph import Graph

# Create an undirected graph
edges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (5, 6), (5, 7), (6, 7), (7, 8)]
g = Graph(edges=edges, directed=False)

# Calculate the cohesive block structure
cohesive_blocks = g.cohesive_blocks()

# Print the membership list for each vertex showing which block a vertex belongs to
print(cohesive_blocks.membership)

# Print the cohesive block hierarchy
for level in range(len(cohesive_blocks)): 
    print(f'Level {level}:', cohesive_blocks[level])
<|EOT|>
