提出HyperQuery模型，提升复杂关系数据的链接预测能力
HyperQuery: Beyond Binary Link Prediction
- 基于超图结构设计新型优化架构，支持高阶关系建模
- 在多个超边预测任务上超越现有方法，性能显著提升
- 适合研究知识图谱、生物网络等复杂系统的研究者
具有复杂集合交集关系的群体是建模各类数据的自然方式，涵盖从社交群体形成到构成生命基础的蛋白质相互作用。以超图形式表示这些高阶关系是一种有效途径。然而，目前将机器学习技术应用于超图结构数据的工作仍有限。本文针对知识超图与简单超图中的链接预测问题，提出一种新颖、简洁且高效的优化架构，同时引入基于节点聚类的特征提取新方法，并证明融合节点标签信息可提升系统表现。所提出的自监督方法在多个超边预测与知识超图补全基准上均取得显著优于当前最优基线的结果。
原文摘要 · Abstract (English)
Groups with complex set intersection relations are a natural way to model a wide array of data, from the formation of social groups to the complex protein interactions which form the basis of biological life. One approach to representing such higher order relationships is as a hypergraph. However, efforts to apply machine learning techniques to hypergraph structured datasets have been limited thus far. In this paper, we address the problem of link prediction in knowledge hypergraphs as well as simple hypergraphs and develop a novel, simple, and effective optimization architecture that addresses both tasks. Additionally, we introduce a novel feature extraction technique using node level clustering and we show how integrating data from node-level labels can improve system performance. Our self-supervised approach achieves significant improvement over state of the art baselines on several hyperedge prediction and knowledge hypergraph completion benchmarks.
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