<｜begin▁of▁sentence｜>### Instruction:
In the soothing realm of academic collaboration, much like a garden of intertwined herbs, researchers flourish through their interconnected efforts, akin to the roots and blooms sharing life's sustenance. As a herbalist tenderly observes the natural alliances formed within their verdant sanctuary, a scholar of network flora seeks to discern the most harmonious clusters within a scholarly ecosystem. Each delicate node in this network is akin to a sage researcher, with the strands of collaboration being the vital link akin to the tendrils that entwine them in shared scholarly pursuit. 

The pursuit for understanding is much like seeking the purest essence of a herb, for which the learned scientist wishes to employ the Leiden algorithm within the graspologic vine to reveal the underlying structure of these academic clusters. Imagine, if you will, a potion that clarifies the dense forest of collaborations into intelligible partitions, revealing the most closely-nit circles of intellectual communion.

In our scenario, how might one invoke the spells of `graspologic` to harness the Leiden algorithm, so as to unfurl the tapestry of communal connections, ensuring that each cluster of researchers, like clusters of herbs, is grouped by the frequency and strength of their collaborative roots?
### Response:
import networkx as nx
from graspologic.partition import leiden

# Construct an undirected graph
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)])

# Optional: add weights to the edges
for edge in G.edges():
    G[edge[0]][edge[1]]['weight'] = 1.0

# Run the Leiden algorithm
community_map = leiden(graph=G)
print(community_map)
<|EOT|>
