<｜begin▁of▁sentence｜>### Instruction:
A researcher has multiple social network graphs, each with the same number of nodes, representing different communities' interactions. The nodes represent individuals, and the edges represent the interactions between them. The researcher is interested in obtaining a low-dimensional representation of each graph to compare the social structures of different communities. How can the researcher use mug2vec from graspologic to generate feature vectors for each social network graph in order to analyze and compare their structural properties?
### Response:
import networkx as nx
from graspologic.embed import mug2vec
from graspologic.simulations import er_np

# Step 1: Generate synthetic graphs to represent multiple social networks
n_graphs = 3
n_nodes = 50
p_edge = 0.2

graphs = [er_np(n_nodes, p_edge) for _ in range(n_graphs)]

# Step 2: Use mug2vec to find a feature vector for each graph
# We'll use the default parameters for pass_to_ranks
embedding_model = mug2vec(
    pass_to_ranks='simple-nonzero',
    omnibus_components=None,
    omnibus_n_elbows=2,
    cmds_components=None,
    cmds_n_elbows=2
)
graph_embeddings = embedding_model.fit_transform(graphs)

# The resulting graph_embeddings array contains the feature vector for each graph.
print(graph_embeddings)
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