标题： 启发式信息引导的多层网络链接预测专家混合模型 显示英文标题

标题： Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks

摘要： 链接预测算法对于多层网络原则上需要有效地考虑整个分层结构，同时捕捉每一层提供的独特上下文。 然而，许多现有的方法在某些层中能够准确预测特定链接，但在其他层中却表现不佳，因为它们未能有效利用不同网络层中编码的多样化信息。 在本文中，我们提出了MoE-ML-LP，这是首个专为多层链接预测设计的专家混合（MoE）框架。 基于多层链接预测的启发式方法，MoE-ML-LP综合了不同专家做出的决策，从而显著增强了预测能力。 我们在真实世界和合成网络上的广泛实验评估表明，MoE-ML-LP始终优于多个基线和其他方法，在平均倒数排名上提高了+60%，在Hits@1上提高了+82%，在Hits@5上提高了+55%，在Hits@10上提高了+41%。 此外，MoE-ML-LP具有模块化架构，能够在无需重新训练整个框架的情况下无缝集成新开发的专家，从而提高效率和可扩展性，为链接预测的未来发展铺平道路。 显示英文摘要

摘要： Link prediction algorithms for multilayer networks are in principle required to effectively account for the entire layered structure while capturing the unique contexts offered by each layer. However, many existing approaches excel at predicting specific links in certain layers but struggle with others, as they fail to effectively leverage the diverse information encoded across different network layers. In this paper, we present MoE-ML-LP, the first Mixture-of-Experts (MoE) framework specifically designed for multilayer link prediction. Building on top of multilayer heuristics for link prediction, MoE-ML-LP synthesizes the decisions taken by diverse experts, resulting in significantly enhanced predictive capabilities. Our extensive experimental evaluation on real-world and synthetic networks demonstrates that MoE-ML-LP consistently outperforms several baselines and competing methods, achieving remarkable improvements of +60% in Mean Reciprocal Rank, +82% in Hits@1, +55% in Hits@5, and +41% in Hits@10. Furthermore, MoE-ML-LP features a modular architecture that enables the seamless integration of newly developed experts without necessitating the re-training of the entire framework, fostering efficiency and scalability to new experts, paving the way for future advancements in link prediction.