用专家混合模型提升多层网络链接预测准确率
Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks
- 基于多层网络启发式规则构建专家系统，融合不同层信息
- 在多个数据集上实现最高60%的平均倒数排名提升
- 模块化设计支持新专家无缝接入，适合持续优化场景
多层网络的链接预测算法需有效利用整体分层结构并捕捉各层特有上下文。然而，现有方法虽在特定层表现良好，却难以兼顾其他层，因未能充分挖掘跨层差异信息。本文提出首个专为多层链接预测设计的专家混合（MoE）框架——MoE-ML-LP。该框架基于多层启发式规则，融合多个专家的决策，显著提升预测能力。在真实与合成网络上的广泛实验表明，相比基线方法，MoE-ML-LP在均值倒数排名（MRR）上提升60%，在Hits@1上提升82%，在Hits@5上提升55%，在Hits@10上提升41%。此外，其模块化架构可无需重新训练即可集成新专家，提升效率与可扩展性，为未来链接预测发展提供新路径。
原文摘要 · Abstract (English)
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.
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