<｜begin▁of▁sentence｜>### Instruction:
Given a graph with edges (0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4), (2, 4), calculate the square clustering coefficient for node 0, as well as for all nodes in the graph. What does the square clustering coefficient tell you about these nodes in terms of the probability of their shared neighbors also being neighbors with one another?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add edges to the graph (nodes are added automatically)
G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4), (2, 4)])

# Compute the square clustering coefficient for node 0
square_clustering_0 = nx.square_clustering(G, 0)

# Compute the square clustering coefficient for all nodes in the graph
square_clustering_all = nx.square_clustering(G)

print('Square clustering for node 0:', square_clustering_0)
print('Square clustering for all nodes:', square_clustering_all)
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