Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple graph is rather under-studied by the current relational learning research, we go one step further and focus on node regression problem on multi-relational graphs. We take inspiration from the well-known label propagation algorithm aiming at completing categorical features across a simple graph and propose a novel propagation framework for completing missing continuous features at the nodes of a multi-relational and directed graph. Our multi-relational propagation algorithm is composed of iterative neighborhood aggregations which originate from a relational local generative model. Our findings show the benefit of exploiting the multi-relational structure of the data in several node regression scenarios in different settings.
翻译:近些年来,以多种关系图可以方便地描述为多关系图的丰富结构信息所收集的现实世界数据有所增加。 简单图表中连续节点特征的推论在当前的关系学习研究研究中研究不足,但我们更进一步,把焦点放在多关系图中的节点回归问题上。 我们从一个众所周知的标签传播算法中得到灵感,该算法的目的是在一个简单图中完成绝对特征,并提出一个新的传播框架,用于完成多关系和定向图形节点上缺失的连续特征。 我们的多关系传播算法是由来自关系当地基因模型的迭接区群组成。 我们的发现显示在不同环境中利用数据在多个节点回归假想中的多关系结构的好处。