Continuous Graph Flow
arXiv:1908.02436
Abstract
In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data. Once learned, the model can be applied to an arbitrary graph, defining a probability density over the random variables represented by the graph. It is formulated as an ordinary differential equation system with shared and reusable functions that operate over the graphs. This leads to a new type of neural graph message passing scheme that performs continuous message passing over time. This class of models offers several advantages: a flexible representation that can generalize to variable data dimensions; ability to model dependencies in complex data distributions; reversible and memory-efficient; and exact and efficient computation of the likelihood of the data. We demonstrate the effectiveness of our model on a diverse set of generation tasks across different domains: graph generation, image puzzle generation, and layout generation from scene graphs. Our proposed model achieves significantly better performance compared to state-of-the-art models.
References in corpus (18)
- Statistical mechanics of complex networks
- Auto-Encoding Variational Bayes
- Inductive Representation Learning on Large Graphs
- Neural Message Passing for Quantum Chemistry
- WaveNet: A Generative Model for Raw Audio
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Loopy Belief Propagation for Approximate Inference: An Empirical Study
- NICE: Non-linear Independent Components Estimation
- Variational Inference with Normalizing Flows
- Variational Graph Auto-Encoders
- Kronecker Graphs: An Approach to Modeling Networks
- GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
- Learning Deep Generative Models of Graphs
- Composing graphical models with neural networks for structured representations and fast inference
- Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
- Variational Message Passing with Structured Inference Networks
- Mean-Field Networks
- Belief Propagation in Conditional RBMs for Structured Prediction