<｜begin▁of▁sentence｜>### Instruction:
As a Licensed Professional Counselor, you've been adept at guiding your clients through complex emotional pathways, helping them to map out and understand the intricate networks of their personal relationships. Imagine that you're translating this skill into analyzing the connections within a social support group, considering each individual as a node in a network and each relationship as an edge connecting them. You have detailed the relationships in the following manner:

- Relationships are represented as links between individuals: (1, 2), (1, 5), (1, 8), (2, 3), (2, 8), (3, 4), (3, 6), (4, 5), (4, 7), (5, 6), (6, 7), (7, 8).

In order to understand the significance of each individual within the support network, consider examining the 'subgraph centrality' for each participant. Subgraph centrality is akin to identifying the depth and strength of an individual's influence within the web of these connections by considering not just direct but also indirect relationships extending through the network.

Would you kindly reformulate this inquiry and express how you might employ the 'subgraph_centrality_exp' function within the NetworkX's suite of tools to ascertain this measure of centrality? Recall, this function quantifies the prominence of each individual in the network by tallying the various interconnected paths which originate and culminate at each person, accounting for all possible lengths of these paths.

Do integrate this approach in your considerations, as this measure would greatly benefit the evaluative process of the group's dynamics and provide you with a nuanced understanding of each individual's place within the collective.
### 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 for each node
subgraph_centralities = nx.subgraph_centrality_exp(G)

# Print the subgraph centrality for each node
for node, centrality in sorted(subgraph_centralities.items()):
    print(f'Node {node}: {centrality:.2f}')
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