Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics.
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.
Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry.
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
Our model generates graphs one block of nodes and associated edges at a time.
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied together: scene graphs are generated on top of objects detected in an image with their pairwise relationship predicted, while region captioning gives a language description of the objects, their attributes, relations, and other context information.
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.