GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
arXiv:1802.03480
Abstract
Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of molecule generation.
References in corpus (9)
- Emergence of scaling in random networks
- Auto-Encoding Variational Bayes
- Neural Message Passing for Quantum Chemistry
- Geometric deep learning: going beyond Euclidean data
- Categorical Reparameterization with Gumbel-Softmax
- Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
- A note on the evaluation of generative models
- Generative Moment Matching Networks
- Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Cited by in corpus (12)
- A Gentle Introduction to Deep Learning for Graphs
- Conditional molecular design with deep generative models
- Molecular graph generation with Graph Neural Networks
- Multilevel Graph Matching Networks for Deep Graph Similarity Learning
- Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
- An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling
- TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
- Deep Generation of Heterogeneous Networks
- Mask-GVAE: Blind Denoising Graphs via Partition
- Deep Generative Models to Extend Active Directory Graphs with Honeypot Users
- CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
- Deep Generative Modeling in Network Science with Applications to Public Policy Research