<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tending to a vibrant garden, where each of your favorite plants—let's call them Plant 0, Plant 1, Plant 2, and Plant 3—has a unique relationship with the others, connected by pathways of pollination or root systems, akin to friendships within your botanical community. Each connection represents a vital pathway where interactions occur, much like the edges in a graph. For instance, pathways could be from Plant 0 to Plant 1, Plant 0 to Plant 2, Plant 0 to Plant 3, Plant 1 to Plant 2, Plant 1 to Plant 3, and Plant 2 to Plant 3.

In cultivating the most harmonious garden, you are interested in understanding which plants hold a central position in this network—essentially, which ones could be considered the most accessible based on their connections. To determine this, you want to calculate the closeness centrality of each plant in your garden network. How would you use NetworkX, a tool akin to your gardening toolkit, to discern the closeness centrality of each plant based on the established connections? This is akin to uncovering which plant is the 'heart' of your garden, enjoying the most direct pathways to all other plants.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)])

# Compute the closeness centrality of the nodes
centrality = nx.closeness_centrality(G)

# Print the closeness centrality
for node in centrality:
    print('Node', node, ':', centrality[node])
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