从影响传播视角进行多关系图上的链接预测
Link prediction on multi-relational graphs from an influence propagation perspective
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中文总结 AI 辅助
该研究从影响传播视角，扩展SIR流行病模型并提出IGNP框架，在真实数据集上大幅提升多关系图链接预测性能。
中文摘要 AI 辅助
预测多关系图中节点间链接（边）的存在性与类型，是从社交交互预测到知识关系识别等应用的关键。利用相关全局信息增强局部特征对准确的链接预测至关重要，但仍具挑战性。我们通过将节点对间的关系建模为节点影响来解决该问题，即节点影响能否传播以及传播的类型，指示了边的位置与类型，这将是预测边最相关的局部与全局信息。为此，我们扩展了易感-感染-恢复（Susceptible-Infectious-Recovered, SIR）流行病模型，以通过子图结构捕获节点的大规模影响传播。随后，这些子图通过虚拟边进行压缩，从而大幅减少利用全局图结构相关的计算。最后，我们提出了影响力图神经预测器（Influential Graph Neural Predictor, IGNP），这是一个受影响传播引导的链接预测框架。大量实验表明，所提方法具有优越性，在广泛使用的真实世界数据集上，其性能大幅优于强大的基线方法。
英文摘要
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.
发表机构
- School of Information Science and Technology, Yunnan Normal University（云南师范大学信息科学与技术学院）
- School of Computing, Macquarie University（麦考瑞大学计算学院）
- School of Computer Science and Engineering, The University of New South Wales（新南威尔士大学计算机科学与工程学院）
- School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）
- School of Computer and Mathematical Sciences, Adelaide University（阿德莱德大学计算机与数学科学学院）
机构由 AI 辅助整理，请以论文原文为准。
