Stanford CS224W: ML with Graphs | 2021 | Lecture 15.1 - Deep Generative Models for Graphs

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Jure Leskovec
Computer Science, PhD
In this lecture, we focus on deep generative models for graphs. We outline 2 types of tasks within the problem of graph generation: (1) realistic graph generation, where the goal is to generate graphs that are similar to a given set of graphs; (2) goal-directed graph generation, where we want to generate graphs that optimize given objectives/constraints. First, we recap the basics for generative models and deep generative models; then, in lectures 15.2 and 15.3 we introduce and focus on GraphRNN, arxiv.org/abs/... one of the first deep generative models for graph; and finally, in lecture 15.4 we discuss GCPN, arxiv.org/abs/... a deep graph generative model designed specifically for application to molecule generation.
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[@ClosiusBeg]

Thanks a lot for the lecture! Is it possible to generate a very regular topologically weighted graph? I mean the mesh, for example polygonal geometry.