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：本文描述了一种利用图生成模型（GGM）进行图分类的新方法。假设一个GGM定义了图及其类别标签上的联合概率分布，我推导了给定图时类别标签概率的分类公式。一种新的条件ELBO可用于训练用于区分的生成式图自编码器模型。虽然利用生成模型进行分类已在非关系独立同分布数据中得到充分探索，但据我们所知，这是图分类的一种新颖方法。