<｜begin▁of▁sentence｜>### Instruction:
Imagine we're in a physiological investigation, where we are analyzing two distinct neural networks, represented by their own synaptic connectivity matrices. These matrices are a result of stochastic block models, and they mirror identical neuronal community structures within the brain. Now, we're eager to compress the information from these high-dimensional spaces into a more tractable, lower-dimensional form for comparison – a process akin to reducing complex physiological phenomena into simpler models for study. To accomplish this task, in the realm of graph theory and network analysis, we would enlist a technique known as Multiple Adjacency Spectral Embedding (MASE) found in the graspologic library. How would one effectively blend the neural networks into this reduced space, while faithfully preserving their shared community architecture, utilizing the MASE approach provided by graspologic?
### Response:
import numpy as np
import networkx as nx
from graspologic.embed import MultipleASE
from graspologic.simulations import sbm

# Simulate two graphs using stochastic block model
n_communities = [50, 50]
p_matrix = [[0.5, 0.1], [0.1, 0.5]]

graph1 = sbm(n_communities, p_matrix)
graph2 = sbm(n_communities, p_matrix)

# Create an instance of MultipleASE
mase = MultipleASE(n_components=2)

# Fit the model to graphs
mase.fit([graph1, graph2])

# Get the embedding of the graph
embedding = mase.transform([graph1, graph2])

print('Embedding shape:', embedding[0].shape)
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