<｜begin▁of▁sentence｜>### Instruction:
Suppose you're assessing the uniqueness of a method for dimensionality reduction applied to network data. The inventors describe a process where they model a complex web of connections using the Erdős–Rényi random graph generation algorithm, specifically generating a graph with 50 interconnected points and a likelihood of each connection forming at 20%. They then aim to translate this intricate network into a simpler 3-dimensional form for easier analysis and visualization.

They propose the utilization of the `adjacency_spectral_embedding` tool from the `graspologic` library to perform this dimensional reduction. The inventors emphasize the accuracy and repeatability of their method by employing a randomized Singular Value Decomposition (SVD) procedure, which introduces stochastic elements into the computation. To address potential concerns regarding the reproducibility of their findings, given the inherent randomness, they assert the implementation of a specific mechanism to fix the randomness source.

For the purposes of intellectual property evaluation, can you confirm whether the inventors have detailed adequate control measures, such as setting a defined 'seed' value, to ensure that the process yields consistent and replicable results each time it is performed?
### Response:
import networkx as nx
from graspologic.pipeline import embed

# Generate a random graph using networkx
n = 50
p = 0.2
G = nx.erdos_renyi_graph(n, p, seed=42)

# Using adjacency_spectral_embedding to find a lower dimensional space representation
embedding = embed.adjacency_spectral_embedding(G, dimensions=3, elbow_cut=None, svd_solver_algorithm='randomized', svd_seed=42)

# The embedding is a numpy array with shape (n, dimensions)
# where n is the number of nodes in the graph
print(embedding.shape)
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