  In the big-data age, tabular data are being generated and analyzed
everywhere. As a consequence, finding and understanding the relationships
between the features in these data are of great relevance. Here, to encompass
these relationships, we propose a graph-based method that allows individual,
group and multi-scale analyses. The method starts by mapping the tabular data
into a weighted directed graph using the Shapley additive explanations
technique. With this graph of relationships, we show that the inference of the
hierarchical modular structure obtained by the Nested Stochastic Block Model
(nSBM) as well as the study of the spectral space of the magnetic Laplacian can
help us identify the classes of features and unravel non-trivial relationships.
As a case study, we analyzed a socioeconomic survey conducted with students in
Brazil: the PeNSE survey. The spectral embedding of the columns suggested that
questions related to physical activities form a separate group. The application
of the nSBM approach not only corroborated with that but allowed complementary
findings about the modular structure: some groups of questions showed a high
adherence with the divisions qualitatively defined by the designers of the
survey. As opposed to the structure obtained by the spectrum, questions from
the class Safety were partly grouped by our method in the class Drugs.
Surprisingly, by inspecting these questions, we observed that they were related
to both these topics, suggesting an alternative interpretation of these
questions. These results show how our method can provide guidance for tabular
data analysis as well as the design of future surveys.
