Spectral–Entropy Network Analysis of Multidimensional Poverty: An Explainable AI Framework for Complex Socioeconomic Systems
ENTROPY, cilt.28, sa.7, ss.1-36, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 28 Sayı: 7
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/e28070746
- Dergi Adı: ENTROPY
- Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Science Citation Index Expanded (SCI-EXPANDED), INSPEC, zbMATH, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-36
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Uşak Üniversitesi Adresli: Evet
Özet
Multidimensional poverty is a socioeconomic problem that results from nonlinear interdependencies
of various socioeconomic indicators such as educational, health, and living
standards indices. This paper considers an XAI-based approach for studying the structural
topology and dependency structures of multidimensional poverty systems. It relies on a
combination of machine learning approaches, network science, spectral graph theory, and
entropy-based complexity measures for revealing the systemic interdependencies between
different poverty indicators. The structure of interdependencies between socioeconomic
indices associated with education is represented by the multilayer perceptron (MLP). The
interpretability of the model is provided via the computation of SHAP values by means
of the KernelSHAP method. Higher-order interactions between variables are revealed
via the construction and analysis of SHAP-based interaction networks. The proposed
methodology is then employed on the GEMPI 2025 dataset consisting of 109 countries. The
results show significant consistency in the structural map (R2 = 0.9890) in combination
with stable internal consistency in cross-validation (R2 = 0.9864, SD = 0.0074). The SHAP
analysis shows that standards of living and health have a high influence on the structural
mapping of education, while the contribution of income-related features is lower compared
to other features. Entropy analysis points toward partially fragmented dependency networks
with a moderate concentration of explanatory influences (H = 1.705). The proposed
framework can be used to characterize structural dependencies, identify influencing system
components, and map informational processes in multidimensional poverty systems by
combining the methodology of explainable artificial intelligence with entropy–spectral
network analysis.