<｜begin▁of▁sentence｜>### Instruction:
Imagine we're in a situation where our company has a network of suppliers and clients represented in a comprehensive database, with relationships and transactions detailed in an adjacency matrix 'A'. We're considering a merger with another company that has a similar network, described by their adjacency matrix 'B'. Both matrices are equal in terms of dimensions.

As the Purchasing Manager looking to streamline the integration process, you're tasked with aligning our company’s existing supplier-client network with that of the merging entity to ensure a smooth transition and to keep operational disruptions to a minimum. 

To achieve this, we must find the optimal realignment – or permutation – of the nodes in the second company's network graph 'B' so that it best corresponds to our original network graph 'A'. The goal here is to minimize any discrepancies between corresponding connections (edge disagreements). In cases where the relationships have different weights (indicating perhaps the volume or value of transactions), we seek to minimize the sum of the squared differences between these weights.

To accomplish this, we would do well to utilize the `graspologic.match.graph_match` function of the graspologic library – a tool specifically designed to handle such graph matching problems. Your role would include overseeing this matching process to ensure that the resulting unified network is as efficient and well-organized as possible.
### Response:
import numpy as np
from graspologic.match import graph_match

# Adjacency matrices representing two graphs A and B
A = np.array([[0, 1, 0],
             [1, 0, 1],
             [0, 1, 0]])
B = np.array([[0, 1, 1],
             [1, 0, 0],
             [1, 0, 0]])

# Graph Matching: Attempt to find a permutation of the nodes of B
# to minimize the number of induced edge disagreements with A
result = graph_match(A, B)

# Print the results
print('Indices in A: ', result.indices_A)
print('Indices in B: ', result.indices_B)
print('Score: ', result.score)
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