<｜begin▁of▁sentence｜>### Instruction:
As a scientist delving into network analysis, imagine you're exploring the structure of social interactions within a research community. You've gathered data on collaborations among 10 researchers, represented as nodes, and their shared projects, depicted by 20 edges in an Erdős-Rényi random graph. Now, you're keen to unravel the hierarchical relationships between cohesive groups within this network. To achieve this, you're considering utilizing the `hierarchy` function from the `CohesiveBlocks` module in the `igraph` library. This function can assist in generating a new graph that illustrates the hierarchical organization of cohesive blocks, shedding light on the clustering and subgrouping dynamics within your research community's collaborations.
### Response:
from igraph import Graph
from igraph import CohesiveBlocks

g = Graph.Erdos_Renyi(n=10, m=20)
cb = CohesiveBlocks(g, g.vs['name'])
block_hierarchy = cb.hierarchy()
print(block_hierarchy)
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