SGVAE: Sequential Graph Variational Autoencoder

12/17/2019
by   Bowen Jing, et al.
0

Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder operating directly on graphical representations of data. In our model, the encoding and decoding of a graph as is framed as a sequential deconstruction and construction process, respectively, enabling the the learning of a latent space. Experiments on a cycle dataset show promise, but highlight the need for a relaxation of the distribution over node permutations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/19/2023

RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design

Identifying molecules that exhibit some pre-specified properties is a di...
research
04/24/2019

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

Graph structured data are abundant in the real world. Among different gr...
research
09/30/2022

Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space

Group-equivariant neural networks have emerged as a data-efficient appro...
research
02/09/2018

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

Deep learning on graphs has become a popular research topic with many ap...
research
09/13/2021

Deep Generative Models to Extend Active Directory Graphs with Honeypot Users

Active Directory (AD) is a crucial element of large organizations, given...
research
09/25/2020

Hierarchical Sparse Variational Autoencoder for Text Encoding

In this paper we focus on unsupervised representation learning and propo...
research
05/21/2021

Towards Automatic Sizing for PPE with a Point Cloud Based Variational Autoencoder

Sizing and fitting of Personal Protective Equipment (PPE) is a critical ...

Please sign up or login with your details

Forgot password? Click here to reset