From Graph Generation to Graph Classification

• URL: http://arxiv.org/abs/2302.07989v3
• Date: Sun, 23 Jul 2023 20:21:48 GMT
• Title: From Graph Generation to Graph Classification
• Authors: Oliver Schulte
• Abstract summary: 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.
• Score: 15.884115251561807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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