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

TL;DR

This paper introduces MoE-ML-LP, a novel Mixture-of-Experts framework for multilayer network link prediction, which effectively combines diverse heuristics to improve accuracy and scalability across various network types.

Contribution

The paper presents the first MoE framework tailored for multilayer link prediction, integrating multiple heuristics for enhanced performance and modularity.

Findings

Achieves +60% in Mean Reciprocal Rank

Achieves +82% in Hits@1

Modular architecture enables easy integration of new experts

Abstract

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.