<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on a visual storytelling project that maps out the paths through which information travels within a network of contacts. In our scenario, we have a collection of photographs representing different locations where news events occur, and we're looking to find out which location is most central to the flow of information, not just through direct connections, but taking into account how easily information can spread from one hub to another through these paths.

We've already established a network representing these relationships, where each photo corresponds to a node. We've notated the connections between these points with lines, marked with numbers indicating the ease of information transfer—the lower the number, the easier the transfer. For instance, locations 'A' and 'B' are connected with a '3', meaning it's moderately easy for information to pass between them, while locations 'A' and 'C' have a '2', suggesting an even easier transfer.

The connections can be summarized as follows:
- 'A' and 'B' with a weight of 3
- 'A' and 'C' with a weight of 2
- 'B' and 'C' with a weight of 6
- 'B' and 'D' with a weight of 1
- 'C' and 'D' with a weight of 2

In terms of our photojournalism endeavor, how could we determine the significance of each location within this network? Specifically, we're interested in understanding the intrinsic role each photo's location plays in dispersing these stories efficiently across the entire network, which in network analysis is known as the current flow closeness centrality. How could we measure this attribute for each node using NetworkX to enhance our visual story?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([('A', 'B', 3), ('A', 'C', 2), ('B', 'C', 6), ('B', 'D', 1), ('C', 'D', 2)])

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

# Print the current flow closeness centrality for each node
print(centrality)
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