<｜begin▁of▁sentence｜>### Instruction:
Imagine we're setting up a photo shoot to capture the intricate social network of a community represented by a sprawling 100-point light display, where each light can potentially connect to another with a 5% chance, mirroring the randomness of real-life interactions. Now, to encapsulate the essence of this network with clarity and simplicity through our lens, we consider utilizing the technique of adjacency spectral embedding. This method would allow us to translate the complex web of connections into a more manageable visual arrangement, akin to distilling the rich narrative of the community into a handful of striking images. How might we employ this technique to effectively condense the grand scale of our light network into a coherent, dimensional photographic composition that still retains the most pivotal elements of its original structure?
### Response:
import networkx as nx
from graspologic.embed import AdjacencySpectralEmbed

# Step 1: Construct a graph
n = 100  # Number of nodes
p = 0.05  # Probability of edge creation
np.random.seed(42)
G = nx.fast_gnp_random_graph(n, p)

# Step 2: Perform adjacency spectral embedding
ase = AdjacencySpectralEmbed(n_components=None, n_elbows=2, algorithm='randomized', n_iter=5, check_lcc=True, diag_aug=True, concat=False, svd_seed=None)
embedding = ase.fit_transform(G)

print(embedding)
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