<｜begin▁of▁sentence｜>### Instruction:
Given a graph G with the nodes {1, 2, 3, 4} and the weighted edges {(1, 2, 0.5), (1, 3, 0.75), (2, 4, 0.6), (3, 4, 0.8), (1, 4, 1)}, how can we calculate the current-flow closeness centrality (information centrality) for the nodes in this graph?
### Response:
import networkx as nx

# Create a undirected graph
G = nx.Graph()

# Add nodes to the graph
G.add_nodes_from([1, 2, 3, 4])

# Add weighted edges to the graph
G.add_weighted_edges_from([(1, 2, 0.5), (1, 3, 0.75), (2, 4, 0.6), (3, 4, 0.8), (1, 4, 1)])

# Compute information centrality
info_centrality = nx.algorithms.centrality.information_centrality(G, weight='weight')

print(info_centrality)
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