用多输出高斯过程建模图数据的节点与属性关联，灵活高效。
Multi-Output Gaussian Processes for Graph-Structured Data
- 基于多输出高斯过程构建图数据回归框架，融合节点间与属性间相关性。
- 在合成与真实数据上表现优于现有方法，可适配多种数据配置。
- 适用于需要灵活建模图结构相关性的研究者，如社交网络分析。
图结构数据是一类具有图结构关联的数据，其中顶点和边描述某种数据关联。本文提出一种基于多输出高斯过程（MOGP）的图结构数据回归方法，以捕捉顶点间的相关性以及相关数据间的关联性。所提公式基于MOGP的定义构建，具备广泛适用性，且因核函数设计灵活而具有强表达能力。该方法包含现有图结构高斯过程方法作为特例，可消除现有方法在数据配置、模型选择与推理场景上的限制。通过合成数据与真实数据的计算机实验评估了该方法的扩展性能。
原文摘要 · Abstract (English)
Graph-structured data is a type of data to be obtained associated with a graph structure where vertices and edges describe some kind of data correlation. This paper proposes a regression method on graph-structured data, which is based on multi-output Gaussian processes (MOGP), to capture both the correlation between vertices and the correlation between associated data. The proposed formulation is built on the definition of MOGP. This allows it to be applied to a wide range of data configurations and scenarios. Moreover, it has high expressive capability due to its flexibility in kernel design. It includes existing methods of Gaussian processes for graph-structured data as special cases and is possible to remove restrictions on data configurations, model selection, and inference scenarios in the existing methods. The performance of extensions achievable by the proposed formulation is evaluated through computer experiments with synthetic and real data.
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