<｜begin▁of▁sentence｜>### Instruction:
Imagine you are an actuary working on a complex financial risk model. You have two graph representations exhibiting investment scenarios. In the first graph, G1, the nodes represent individual investment components (1, 2, 3, 4), and the edges represent the types of financial relationships between them, labeled 'A' and 'B'. In the second graph, G2, it has a similar structure, but the investment components are different (5, 6, 7, 8), as well as the pattern of financial relationships ('B', 'A', 'A', 'A'). Given these differing structures and financial relationship categories, would it be possible to use the weisfeiler_lehman_graph_hash method to create unique hashes that can differentiate these two financial investment scenarios based on their structure and financial link categories?
### Response:
import networkx as nx
from networkx.algorithms.graph_hashing import weisfeiler_lehman_graph_hash

# Create two graphs
G1 = nx.Graph()
G1.add_edges_from([(1, 2, {'label': 'A'}), (2, 3, {'label': 'A'}), (3, 1, {'label': 'A'}), (1, 4, {'label': 'B'})])
G2 = nx.Graph()
G2.add_edges_from([(5, 6, {'label': 'B'}), (6, 7, {'label': 'A'}), (7, 5, {'label': 'A'}), (7, 8, {'label': 'A'})])

# Hash the graphs without considering edge labels
hash1 = weisfeiler_lehman_graph_hash(G1)
hash2 = weisfeiler_lehman_graph_hash(G2)
print(f'Hash of G1 without edge labels: {hash1}')
print(f'Hash of G2 without edge labels: {hash2}')

# Hash the graphs considering edge labels
hash1_labeled = weisfeiler_lehman_graph_hash(G1, edge_attr='label')
hash2_labeled = weisfeiler_lehman_graph_hash(G2, edge_attr='label')
print(f'Hash of G1 with edge labels: {hash1_labeled}')
print(f'Hash of G2 with edge labels: {hash2_labeled}')
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