<｜begin▁of▁sentence｜>### Instruction:
In the realm of linguistic study, where we often parse complex structures to understand communication patterns, imagine a scenario where you are examining a network of linguistic interactions represented by a set of connections, or 'edges', between different 'nodes' symbolizing language users. The connections in question are as follows: a dialogue link from speaker 1 to speaker 2, another from speaker 1 to speaker 3, a mutual exchange between speakers 2 and 3, an interaction stretch from speaker 3 to speaker 4, a conversational reply from speaker 4 to speaker 2, an introduction from speaker 5 to speaker 1, and lastly, a discussion bridge between speaker 5 and speaker 4.

Within this network, we seek to assess the feasibility of the sequence of interaction frequencies, i.e., the number of exchanges each speaker is engaged in, against a model of theoretical constructs known as 'pseudographs'. In essence, would this sequence of interaction intensities be plausible if we were to synthesize it within the context of pseudographical principles using a computational linguistic tool such as NetworkX? 

Please transpose this abstract inquiry into a more concrete analysis framework, keeping the intricacies of the data and the question intact.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])

# Generate a sequence of degrees from the graph
sequence = [d for _, d in G.degree()]

# Check if the sequence is pseudographical
is_pseudo = nx.is_pseudographical(sequence)

print('Is the sequence pseudographical?', is_pseudo)
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