<｜begin▁of▁sentence｜>### Instruction:
In the intricate web of social interactions that shape our local communities, imagine we have traced the connections among a group of influential individuals—these routes of influence and communication form a network, represented by the ties [(1, 2), (1, 5), (1, 8), (2, 3), (2, 8), (3, 4), (3, 6), (4, 5), (4, 7), (5, 6), (6, 7), (7, 8)]. Through the lens of my journalist's curiosity, I'm aiming to uncover who among these figures holds significant sway – not merely by their direct connections, but through the ripple effect of their influence extending across the entire network.

To bring this analogy to life, can we delve into the science of networks using NetworkX to compute the ‘subgraph centrality’ for each individual within this social matrix? This measure will reveal the leaders whose influence is most pronounced, laying the groundwork for a compelling narrative about the power dynamics within this group.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 5), (1, 8), (2, 3), (2, 8), (3, 4), (3, 6), (4, 5), (4, 7), (5, 6), (6, 7), (7, 8)])

# Calculate subgraph centrality
sc = nx.subgraph_centrality_exp(G)

# Print subgraph centrality for each node
print({node: f'{sc[node]:0.2f}' for node in sorted(sc)})
<|EOT|>
