通过模型平均实现多层网络迁移学习，无需原始数据也能提升预测效果。
A Transfer Learning Framework for Multilayer Networks via Model Averaging
- 采用双层模型平均机制，自动加权跨层与层内模型。
- 在真实推荐系统中验证，预测准确率优于现有方法。
- 适合缺乏原始数据但需跨层知识迁移的场景。
多层网络中的链接预测在推荐系统和蛋白质-蛋白质相互作用预测等应用中至关重要。尽管已有诸多技术，但多数依赖共享结构假设并需访问原始辅助数据，限制了实用性。为此，我们提出一种基于双层模型平均的多层网络迁移学习框架。利用基于边的K折交叉验证准则，自动为跨层和层内候选模型分配权重，实现在无共享结构先验知识下，从辅助层迁移信息并降低模型不确定性。理论上，在温和条件下证明了方法的最优性与权重收敛性；计算上高效且保护隐私，无需共享原始数据，支持多服务器并行处理。模拟实验表明，该方法在预测精度和鲁棒性方面优于其他方法。进一步通过两个真实推荐系统应用验证其实际价值。
原文摘要 · Abstract (English)
Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting their practicality. To address these issues, we propose a novel transfer learning framework for multilayer networks using a bi-level model averaging method. A $K$-fold cross-validation criterion based on edges is used to automatically weight inter-layer and intra-layer candidate models. This enables the transfer of information from auxiliary layers while mitigating model uncertainty, even without prior knowledge of shared structures. Theoretically, we prove the optimality and weight convergence of our method under mild conditions. Computationally, our framework is efficient and privacy-preserving, as it avoids raw data sharing and supports parallel processing across multiple servers. Simulations show our method outperforms others in predictive accuracy and robustness. We further demonstrate its practical value through two real-world recommendation system applications.
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