<｜begin▁of▁sentence｜>### Instruction:
Imagine a scenario where a detective of data, a sleuth in the digital realm, is poring over a series of seemingly unrelated crime scenes—each represented by its own intricate web of connections and interactions, much like small, individual networks. The nodes within these graphs are the consistent cast of characters, though the relationships between them—quantified by unique weights attached to the edges—vary from one network to the next.

Our detective's task? To discern patterns hidden within these networks, unearthing the underlying structure that could reveal clusters of similarity. To achieve this, they've heard whispers of a tool named mug2vec—a method devised for encoding these diverse networks into a uniform space of fixed-length feature vectors.

How might our data detective utilize mug2vec to transpose these graphs into a digestible form, thus enabling them to apply the calculated scrutiny of clustering algorithms or other analytical methods? This is the mystery they seek to unravel, maintaining the integrity of each graph's unique narrative whilst finding common ground upon which to compare them.
### Response:
import networkx as nx
from graspologic.embed import mug2vec

# List of graph objects
graphs = [nx.gnp_random_graph(10, 0.5, seed=i) for i in range(3)]

# Instantiate mug2vec with desired parameters
mug2vec_instance = mug2vec(pass_to_ranks='simple-all', omnibus_components=2, omnibus_n_elbows=2, cmds_components=2, cmds_n_elbows=2, svd_seed=42)

# Fit the model and transform graphs into vector representation
embeddings = mug2vec_instance.fit_transform(graphs)
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