A Multiple Attention Layer-shareable Method for Link Prediction in Multilayer Networks

Link prediction in multilayer networks aims to predict missing links at the target layer by incorporating structural information from both auxiliary layers and the target layer. Existing methods tend to learn layer-specific knowledge to maximize the link prediction performance on a specific network layer. However, they have difficulty incorporating multilayer structural information to improve the link prediction performance. Therefore, we propose a Multiple Attention Layer-shareable Method (MALM) for link prediction in multilayer networks, which consists of a feature encoder, a knowledge learner, and a fusion predictor. The feature encoder introduces multiple attention mechanisms to encode the feature representations of links by differentiating the importance of structural information for each link. In cooperation with the feature encoder, the knowledge learner splits the link prediction tasks into different layers and employs meta-learning to learn layer-shareable knowledge from these link prediction tasks. Finally, the fusion predictor combines the learned layer-shareable knowledge with the layer-specific knowledge at the target layer for link prediction. Experiments on real-world datasets demonstrate that the proposed MALM outperforms existing state-of-the-art baselines in link prediction in multilayer networks.