论文标题
学习属性错失图
Learning on Attribute-Missing Graphs
论文作者
论文摘要
具有完整节点属性的图形最近已被广泛探索。在实践中，有一个图表，其中只有部分节点的属性可用，而其他节点的属性可能完全缺少。此属性错失图与众多现实世界应用有关，并且研究相应的学习问题的研究有限。现有的图形学习方法在内，包括流行的GNN无法提供满意的学习性能，因为它们未针对属性失误的图指定。因此，为这些图形设计新的GNN是图表学习社区的一个燃烧问题。在本文中，我们在图表上做出了共享的空间假设，并开发了一种新型的基于分布的GNN，称为结构 - 属性变压器（SAT），以用于属性错失图。 SAT在脱钩方案中利用结构和属性，并通过分布匹配技术实现结构和属性的联合分布建模。它不仅可以执行链接预测任务，还可以执行新引入的节点属性完成任务。此外，引入了实用措施来量化节点属性完成的性能。在七个现实世界数据集上进行的广泛实验表明，SAT在链接预测和节点属性完成任务上显示出比其他方法更好的性能。代码和数据可在线提供：https：//github.com/xuchensjtu/sat-master-online
Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of the others might be entirely missing. This attribute-missing graph is related to numerous real-world applications and there are limited studies investigating the corresponding learning problems. Existing graph learning methods including the popular GNN cannot provide satisfied learning performance since they are not specified for attribute-missing graphs. Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community. In this paper, we make a shared-latent space assumption on graphs and develop a novel distribution matching based GNN called structure-attribute transformer (SAT) for attribute-missing graphs. SAT leverages structures and attributes in a decoupled scheme and achieves the joint distribution modeling of structures and attributes by distribution matching techniques. It could not only perform the link prediction task but also the newly introduced node attribute completion task. Furthermore, practical measures are introduced to quantify the performance of node attribute completion. Extensive experiments on seven real-world datasets indicate SAT shows better performance than other methods on both link prediction and node attribute completion tasks. Codes and data are available online: https://github.com/xuChenSJTU/SAT-master-online
