<｜begin▁of▁sentence｜>### Instruction:
FADE IN:

INT. DATA SCIENTIST'S OFFICE - DAY

Our PROTAGONIST, a data scientist with a penchant for network analysis and a bit of dramatic flair, faces the GLOW of a computer screen. The room is a medley of whiteboards filled with algorithmic musings and feverish scribbles.

PROTAGONIST
(pondering)
In a world where connections form the very fabric of existence, a vast network of a hundred digital souls emerges, born from the mind of Erdős and Rényi. Each node, a beacon capable of forming links with its fellows at a mere five percent chance—truly, a dance of randomness and structure.

(beat)
But amidst this algorithmic cosmos lies a deeper narrative, a tale of communities hidden within the chaos, each a cluster of interconnected destinies.

Rising from their chair, the PROTAGONIST makes their way to the whiteboard, marker in hand.

PROTAGONIST (CONT'D)
(to the whiteboard)
And so, the quest begins with the invocation of 'hierarchical_leiden' from the mystical graspologic library. A spell to unravel the initial and final act of this ensemble—earthly names they call 'clusterings.' Our ensemble, once thought to be a cacophony, shall reveal its true nature—will the protagonists and adversaries uncover the symphony within?

A sigh escapes them as they return to the screen, the cursor blinking, ready to transpose their thoughts into code.

CUT TO:

PROTAGONIST'S FINGERS
Dancing across the keyboard, ready to breathe life into the story of our hundred nodes.

FADE OUT.
### Response:
```python
import networkx as nx
from graspologic.partition import hierarchical_leiden

# Generate a synthetic network
G = nx.erdos_renyi_graph(n=100, p=0.05)

# Run hierarchical leiden algorithm to detect communities
hierarchical_clusters = hierarchical_leiden(G)

# Retrieve the initial and final level clusterings
initial_clustering = hierarchical_clusters.first_level_hierarchical_clustering()
final_clustering = hierarchical_clusters.final_level_hierarchical_clustering()

# Display the initial and final clustering of nodes
print('Initial clustering:', initial_clustering)
print('Final clustering:', final_clustering)
```
<|EOT|>
