论文标题
dirichlet图形自动编码器
Dirichlet Graph Variational Autoencoder
论文作者
论文摘要
图形神经网络（GNN）和变分自动编码器（VAE）已被广泛用于建模和生成具有潜在因子的图形。但是，没有明确的解释这些潜在因素以及为什么它们表现良好。在这项工作中，我们将Dirichlet图形图形自动编码器（DGVAE）作为图形构件作为潜在因素。我们的研究将基于VAE的图形生成和平衡的图形剪切连接起来，并提供了一种新的方法来理解和改善基于VAE的图形生成的内部机制。具体而言，我们首先将DGVAE的重建项解释为以原则性的方式切割的平衡图。此外，由平衡图切割中的低通传递特性激发，我们提出了一个名为Heatts的新型GNN变体，将输入图编码为群集成员资格。 Heatts利用Taylor系列来快速计算热核，并且比图形卷积网络（GCN）具有更好的低传递特性。通过对图生成和图形聚类的实验，我们证明了我们提出的框架的有效性。
Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation of what these latent factors are and why they perform well. In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. Our study connects VAEs based graph generation and balanced graph cut, and provides a new way to understand and improve the internal mechanism of VAEs based graph generation. Specifically, we first interpret the reconstruction term of DGVAE as balanced graph cut in a principled way. Furthermore, motivated by the low pass characteristics in balanced graph cut, we propose a new variant of GNN named Heatts to encode the input graph into cluster memberships. Heatts utilizes the Taylor series for fast computation of heat kernels and has better low pass characteristics than Graph Convolutional Networks (GCN). Through experiments on graph generation and graph clustering, we demonstrate the effectiveness of our proposed framework.
