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

arXiv: https://arxiv.org/abs/2501.17557 · HTML: https://mlsanity.com/paper/2501.17557/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts.
- Builds on 0 of its 23 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Placed in

- Mixture-of-experts / [Expert routing strategies](https://mlsanity.com/moe/expert-routing-strategies/)

## Built on by (most cited first, 2 of 2)

- [GMoPE:A Prompt-Expert Mixture Framework for Graph Foundation Models](https://mlsanity.com/paper/2511.03251/) (2025)
- [MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction](https://mlsanity.com/paper/2504.17749/) (2025)

## Abstract (arXiv)

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.