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Article · Applied Sciences · May 7, 2026

Optimization of Regional Budget Allocation Based on Graph Neural Networks: Finding a Balance Between Equality and Efficiency

Gulbakyt Sembina, Almaz Abdualiyev, Long Liu, S.K. Sagnayeva, Айгерим Айтим, Aigul Bissarinova

Aigerim Aitim
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Abstract

Uneven regional socioeconomic growth presents a fundamental economic challenge, as traditional budget allocation models frequently fall short by neglecting spatial topology and spillover effects. By striking a statistically correct balance between social equality and economic efficiency, this study seeks to maximize regional public funds. We present a new hybrid approach that uses Graph Attention Networks (GAT) in conjunction with multi-criteria optimization to model administrative districts as undirected graphs. By combining regularizations for temporal stability and spatial smoothness, the composite loss function scalarizes a logarithmic utility function for efficiency and a differentiable Gini index for equity. The GNN-based model removed major historical funding imbalances, reducing the highest regional funding gap from an 80-fold difference to a 1.08 ratio and reducing per capita funding variance by 92%, according to empirical testing conducted in Kazakhstan’s Almaty region. The system automatically reorganized 208.5 billion KZT (~463 million USD) at the ideal Pareto frontier (α = 0.5), allocating 96.4% of investments to the digitalization sector, which was given priority. According to the results, incorporating spatial topology, which explains 58.7% of choice variability, offers a highly scalable, empirically supported method for improving strategic public administration and reducing institutional stagnation.

Local Government Finance and DecentralizationEconomic Growth and Fiscal PoliciesRegional Economic and Spatial Analysisregional budget allocationfiscal decentralizationgraph attention networks

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