<｜begin▁of▁sentence｜>### Instruction:
Imagine you're an insurance agent examining the robust interconnected network of clients and their referrals. Consider a scenario where you're analyzing client referrals within an undirected connection graph. You've observed the following referral patterns: client 0 referred client 1, client 1 referred client 2, client 2 referred client 3, client 3 circled back to refer client 0, and additionally, client 0 referred client 2.

In this network of referrals, you want to identify the extent of a closely-knit group where everyone is referring everyone else within it – akin to discovering the size of the largest 'insurance advocacy group,' if you will. Could you determine the magnitude of this tight-knit referral circle using the data provided?
### Response:
import networkx as nx
from networkx.algorithms.approximation import large_clique_size

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

# Find the size of a large clique
clique_size = large_clique_size(G)
print(f'The size of a large clique in the graph: {clique_size}')
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