A Deep Citation Network Analysis-Based Decision Optimization Model for Digital Collection Resources
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Abstract
The rapid expansion of digital resource repositories and constrained library budgets have created increasing challenges for the efficient allocation of scholarly information resources, particularly in electromagnetic information transmission environments where high-quality scientific knowledge dissemination relies on advanced digital infrastructures. This study proposes an optimization model integrating Graph Neural Networks and multi-feature fusion to provide quantitative decision support for digital collection development. A heterogeneous citation network is first constructed from large-scale scholarly data, and a Graph Attention Network is employed to learn node representations from which three core characteristics—academic influence, interdisciplinarity, and knowledge freshness—are extracted. A game theory-based combined weighting strategy is subsequently introduced to fuse these complementary features into a comprehensive value metric, and a 0–1 integer programming model is formulated to maximize overall resource value under budget constraints. Simulation experiments based on the public Aminer academic graph dataset compare the proposed approach with conventional impact factor- and usage-oriented strategies. Results demonstrate that the recommended resource portfolio achieves superior long-term academic influence and more balanced disciplinary coverage while maintaining computational interpretability. The proposed framework offers a scalable and data-driven solution for intelligent digital collection management and provides methodological support for efficient scientific information propagation and resource optimization within modern electromagnetic-enabled knowledge infrastructures.
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