While a wide range of interpretable generative procedures for graphs exist,
matching observed graph topologies with such procedures and choices for its
parameters remains an open problem. Devising generative models that closely
reproduce real-world graphs requires domain knowledge and time-consuming
simulation. While existing deep learning approaches rely on less manual
modelling, they offer little interpretability. This work approaches graph
generation (decoding) as the inverse of graph compression (encoding). We show
that in a disentanglement-focused deep autoencoding framework, specifically
Beta-Variational Autoencoders (Beta-VAE), choices of generative procedures and
their parameters arise naturally in the latent space. Our model is capable of
learning disentangled, interpretable latent variables that represent the
generative parameters of procedurally generated random graphs and real-world
graphs. The degree of disentanglement is quantitatively measured using the
Mutual Information Gap (MIG). When training our Beta-VAE model on ER random
graphs, its latent variables have a near one-to-one mapping to the ER random
graph parameters n and p. We deploy the model to analyse the correlation
between graph topology and node attributes measuring their mutual dependence
without handpicking topological properties.

本文提出一种利用 Beta-VAE 深度学习框架对图表现进行生成建模与压缩编码的方法，并成功实现了对随机图与真实图的自动生成，得到了可解释的潜空间变量，在不手动选择拓扑属性情况下，分析了图拓朴与节点属性之间的相关性。