<｜begin▁of▁sentence｜>### Instruction:
As an organizational psychologist, you are fascinated by the interplay between brain connectivity and cognitive functions within a professional setting. You are examining whether variations in cognitive abilities among employees are mirrored in the structural makeup of their brain networks. To this end, you've utilized the mug2vec framework to generate a series of feature vectors, each encapsulating the unique connectivity pattern of an individual's brain network. These vectors, situated within a multidimensional space, now hold the distilled essence of each person's cognitive map.

You're now poised to translate these intricate vectors into meaningful insights that could potentially enrich organizational development and personnel strategies. The challenge lies in interpreting the spatial arrangement of these vectors: how might one skillfully extract and portray the nuanced differences among these brain networks? By doing so, you aim to craft a visual narrative that reveals the cognitive diversity within your workforce, thereby guiding interventions aimed at optimizing team dynamics and individual performance within the company.

In order to methodically approach this task, consider how the position of each vector in relation to others could inform you about the structural brain differences reflecting individual cognitive variations. How might you visually represent this information to make it accessible and informative for stakeholders interested in enhancing the organizational climate and boosting cognitive synergy among team members?
### Response:
import networkx as nx
from graspologic.embed import mug2vec

# Create a list of random graphs
graphs = [nx.gnp_random_graph(100, 0.5) for _ in range(3)]

# Instantiate mug2vec
m2v = mug2vec(pass_to_ranks='simple-all', omnibus_components=5, cmds_components=2)

# Learn a feature vector for each graph
vectors = m2v.fit_transform(graphs)

print('Learned vectors for each graph:')
for i, vec in enumerate(vectors):
    print(f'Graph {i}: {vec}')
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