<｜begin▁of▁sentence｜>### Instruction:
Imagine you're orchestrating a fundraiser event and have a network of 5 potential philanthropic individuals labeled [1, 2, 3, 4, 5]. You're aware of collaborations that have transpired among them, represented as partnerships [(1, 2), (1, 3), (2, 4), (3, 5), (4, 5)]. To maximize the impact of your event, you want to identify a subgroup of these individuals that are closely connected, forming a powerful clique for advocacy and funding. At the same time, you recognize the value in finding those who operate independently - thus, you also seek to determine the largest set within this network whose members do not directly collaborate, ensuring a broad representation of interests.

In essence, you're keen on applying the 'ramsey_r2' function from your organizational toolkit to discern the optimal coalition of closely-knit benefactors (the largest clique) as well as to pinpoint the most expansive circle of autonomous patrons (the largest independent set) within this social graph. Could you reformulate your approach utilizing this method to the scenario as described?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add nodes to the graph
G.add_nodes_from([1, 2, 3, 4, 5])

# Add edges to the graph
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 5), (4, 5)])

# Compute the largest clique and largest independent set
clique, independent_set = nx.algorithms.graph_clique_number.ramsey_r2(G)

print('Largest Clique:', clique)
print('Largest Independent Set:', independent_set)
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