这篇论文把传染病传播模型用到链接预测上，搞了个IGNP框架，在真实数据集上比现有方法强不少。
该论文提出名为 IGNP 的链接预测框架，将节点对间关系建模为影响传播。方法扩展 SIR 流行病模型，通过子图结构捕捉大规模节点影响。IGNP 利用虚拟边压缩子图，显著降低全局图结构的计算开销。在公开真实数据集上，IGNP 大幅优于现有强基线。
Link prediction on multi-relational graphs from an influence propagation perspective
Predicting the existence and type of links (edges) between nodes in a multi-relational graph is key for applications from social interaction prediction to knowledge relationship identification. Enhancing local features with relevant global information is crucial for accurate link prediction, yet it remains challenging. We address this by modeling the relationship between node pairs as node influence. That is, whether the node influence can be propagated and what type of influence is propagated indicates where and what type the edge is, which will be the most relevant local and global information to predict the edges. To this end, we extend the Susceptible-Infectious-Recovered (SIR) epidemic model to capture the influence propagation of nodes on a large scale through sub-graph structures. Subsequently, these sub-graphs are compressed using virtual edges, thereby substantially reducing the computation associated with utilizing the global graph structure. Finally, we propose the Influential Graph Neural Predictor, referred to as IGNP, a link prediction framework guided by influence propagation. Extensive experiments demonstrate the superiority of the proposed method, which outperforms strong baselines by a large margin on the widely used and real-world datasets.