Iteration Continuous Graph Flow Time Continuous Discrete neural message passing 1 2 K Message functions Node Continuous dynamics
In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model distributions of graph structured complex data. The model is formulated as an ordinary differential equation system with shared and reusable functions that operate over the graph structure. This leads to a new type of neural graph message passing scheme that performs continuous message passing over time. This class of models offer several advantages: (1) modeling complex graphical distributions without rigid assumptions on the distributions; (2) not limited to modeling data of fixed dimensions and can generalize probability evaluation and data generation over unseen subset of variables; (3) the underlying continuous graph message passing process is reversible and memory efficient. We demonstrate the effectiveness of our model on two generation tasks, namely, image puzzle generation, and layout generation from scene graphs. Compared to unstructured and structured latent space VAE models, we show that our proposed model achieves significant performance improvement (up to 400% in negative log likelihood)