报告时间：2025年5月23日（星期五）上午10:20

报告地点：翡翠科教楼A座第二会议室

报  告  人：张新雨 研究员

工作单位:  中国科学院数学与系统科学研究院

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报告摘要：

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. Our approach introduces a K-fold cross-validation criterion based on edges to automatically weight inter-layer and intra-layer candidate models. This enables the transfer of information from auxiliary layers while mitigating model selection 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.

报告人简介：

张新雨，中科院数学与系统科学研究院研究员, 中国科学技术大学博士生导师。主要从事计量经济学和统计学理论和应用研究工作，具体研究方向包括模型平均方法及其在经济预测、管理统计、机器学习和生物医学等领域的交叉研究。担任SCI期刊《JSSC》领域主编、《系统科学与数学》等多个期刊的编委，曾获中国青年科技奖，先后主持国家自然科学基金C、B、A和A延续项目。