<｜begin▁of▁sentence｜>### Instruction:
In the realm of fostering a vibrant and cohesive online community, akin to the Karate Club graph—a staple in the network analysis domain—I'm curious about how we might tap into the power of NetworkX to unveil the current-flow closeness centrality scores for each participant within our digital social structure. This metric could provide us with insights into the most connected individuals, much like it delineates the influential members within the said club. Suppose our community interactions were mapped out within a .gml file, which could be analogous to nodes and edges in a graph. How would we proceed to extract this particular measure of centrality for each node, thereby equipping us with the knowledge of who truly lies at the heart of our community's interaction network?
### Response:
import networkx as nx
import numpy as np

# Create a graph
G = nx.karate_club_graph()

# Compute the current-flow closeness centrality for the graph
# Using default parameters for weight, dtype, and solver
centrality = nx.current_flow_closeness_centrality(G)

# Print the current-flow closeness centrality for each node
for node in centrality:
    print(f'Node {node}: {centrality[node]}')
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