<｜begin▁of▁sentence｜>### Instruction:
Given a random undirected graph with 100 nodes and a probability of edge creation of 0.05, how can we represent it in a lower-dimensional space while capturing the graph's structure more comprehensively than a simple adjacency embedding? Use Laplacian Spectral Embedding from the graspologic library to perform this task and then print the resulting embeddings.
### Response:
import networkx as nx
from graspologic.pipeline import laplacian_spectral_embedding

# Generate a random undirected graph
G = nx.erdos_renyi_graph(n=100, p=0.05)

# Apply Laplacian Spectral Embedding with specific parameters
embedding = laplacian_spectral_embedding(G, form='DAD', dimensions=2, elbow_cut=None, svd_solver_algorithm='randomized', svd_solver_iterations=5, svd_seed=None, weight_attribute='weight')

# Print the embedding to see the lower-dimensional space representation
print(embedding)
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