<｜begin▁of▁sentence｜>### Instruction:
Imagine you're prepping for an avant-garde piece on the interconnected influences of various fashion trends and icons, visualizing them as a network of glittering stars on the runway of your latest blog post. You've cleverly decided to map out the relationships by representing each major fashion trend or icon as a node in a dazzling network, with weighted ties that symbolize the strength of influence or collaboration between them.

Now, picture each connection in this haute couture cosmos having a certain "capacity," much like a weighted edge in a graph, to represent the intensity of the collaboration. You've noted down some of the initial relationships as follows: The synergy between Vintage and Boho trends has a weight of 3, the influence Chic has on Vintage holds a weight of 2, Boho influencing Chic has the lightest weight of 1, and finally, the spread from Chic to Avant-garde emerges with the heaviest weight of 4.

What you're striving for is to gauge the trendsetting potential or, let's say, the "runway flow" power of each trend or icon. To put it in the lingo of your blog readers: "Who is the most pivotal influencer in the fashion network?" To unveil this key figure, you're pondering on how to calculate the "current-flow closeness centrality" (or in more thematic terms, the "information centrality") for each fashion node in this chic graph, treating the weights you have assigned to each relationship as the capacity for trend transition or information flow.

Would it pique your readers' interest to discover which style holds the crown for the ultimate influencer, according to this metric? Let's find out who truly rules the runway.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 3), (1, 3, 2), (2, 3, 1), (3, 4, 4)])

# Compute the current-flow closeness centrality
centrality = nx.information_centrality(G, weight='weight')

print('Current-flow closeness centrality:')
print(centrality)
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