<｜begin▁of▁sentence｜>### Instruction:
In the intricate web of political alliances represented by a network with connections such as [(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (4, 5)], one might be interested in evaluating the strength and depth of these relationships at an individual level. Could you elucidate how to apply NetworkX to measure the square clustering coefficient for each political figure within this intricate tapestry, to gauge the tightness of their immediate political circles, thus offering a quantitative dimension to their influence and the cohesion of their alliances?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (4, 5)])

# Calculate square clustering coefficient
square_clustering_coefficient = nx.square_clustering(G)

print(square_clustering_coefficient)
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