This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and their class labels, I derive classification formulas for the probability of a class label given a graph. A new conditional ELBO can be used to train a generative graph auto-encoder model for discrimination. While leveraging generative models for classification has been well explored for non-relational i.i.d. data, to our knowledge it is a novel approach to graph classification.
翻译:本说明介绍了一种利用图形基因化模型(GGM)对图表进行分类的新方法。假设GGM界定了图表及其类标签之间的共同概率分布,我为给定图表的分类标签概率得出分类公式。新的有条件的ELBO可用于对歧视进行基因化图形自动编码模型的培训。虽然在非关联性i.d.数据方面已经很好地探索了利用基因化模型进行分类,但根据我们的知识,这是对图表分类的一种新颖的方法。