<｜begin▁of▁sentence｜>### Instruction:
Sure, imagine you're crafting a digital marketing strategy for a new online networking platform designed to connect professionals across various industries. To optimize user engagement within this digital ecosystem, you've modeled the platform's interactions as a network, where nodes represent users 1 through 8, and the connections (or edges) between them represent various communication paths: (1, 2), (1, 5), (1, 8), (2, 3), (2, 8), (3, 4), (3, 6), (4, 5), (4, 7), (5, 6), (6, 7), and (7, 8).

As an innovative part of your strategy, you want to identify the most influential users—those who are central to communication flow and community building. To do this quantitatively, you've decided to utilize the 'subgraph_centrality_exp' metric from the networkx library to evaluate the influence each user holds within the network.

Could you apply this advanced metric to assess the subgraph centrality for each user in the network? Subgraph centrality offers a nuanced insight by considering not just direct connections but the sum of the influence of all user interactions, both direct and indirect. This metric will help fine-tune your marketing efforts towards those individuals that could be key opinion leaders or central figures in the network, increasing the efficacy of your campaign.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Adding edges to the 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 the subgraph centrality for each node
subgraph_centrality = nx.subgraph_centrality_exp(G)

# Display the subgraph centrality for each node
for node in sorted(subgraph_centrality):
    print(f'{node} {subgraph_centrality[node]:0.2f}')
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