从影响传播视角建模多关系图链接预测，提升准确率。
Link prediction on multi-relational graphs from an influence propagation perspective
- 将节点间关系视为影响传播，用改进的SIR模型捕捉全局影响。
- 通过虚拟边压缩子图，显著降低全局结构计算开销。
- 在真实数据集上超越主流方法，适合知识图谱等场景使用。
在多关系图中预测节点间链接的存在与类型，对社交关系预测和知识关系识别等应用至关重要。增强局部特征与相关全局信息的结合是提高预测准确性的关键，但依然具有挑战性。本文提出将节点对之间的关系建模为节点影响传播：影响能否传播及传播类型决定了链接的位置与类型，这正是最相关的局部与全局信息。为此，我们扩展了经典的易感-感染-恢复（SIR）流行病模型，通过子图结构大规模捕捉节点影响传播。随后，利用虚拟边对这些子图进行压缩，大幅减少使用全局图结构带来的计算负担。最后，提出基于影响传播引导的链接预测框架——影响力图神经预测器（IGNP）。大量实验表明，该方法在广泛使用的现实世界数据集上显著优于现有强基线。
原文摘要 · Abstract (English)
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.
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