<｜begin▁of▁sentence｜>### Instruction:
Given a directed graph whose edge set is [('A', 'B', 1), ('B', 'C', 2), ('C', 'A', 3), ('B', 'D', 4)] with node personalization values {'A': 0.1, 'B': 0.2, 'C': 0.3, 'D': 0.4} and a dangling node 'A', how would you compute the Google matrix using NetworkX with a damping factor of 0.9?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edge('A', 'B', weight=1)
G.add_edge('B', 'C', weight=2)
G.add_edge('C', 'A', weight=3)
G.add_edge('B', 'D', weight=4)

# Define personalization and dangling dictionaries
personalization = {'A': 0.1, 'B': 0.2, 'C': 0.3, 'D': 0.4}
dangling = {'A': 1}

# Compute the Google matrix of the graph
google_matrix = nx.google_matrix(G, alpha=0.9, personalization=personalization, dangling=dangling)

print('Google matrix:\n', google_matrix)
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