DeepGG: a Deep Graph Generator
This work addresses graph generation for applications like drug discovery and network analysis, but it appears incremental as it builds on existing state machine ideas with specific embedding techniques.
The authors tackled the problem of learning generative models of graphs by introducing an improved framework based on deep state machines, achieving models that can handle graphs of up to 150 vertices with statistical tests to evaluate distribution representation.
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more. We present an improved framework for learning generative models of graphs based on the idea of deep state machines. To learn state transition decisions we use a set of graph and node embedding techniques as memory of the state machine. Our analysis is based on learning the distribution of random graph generators for which we provide statistical tests to determine which properties can be learned and how well the original distribution of graphs is represented. We show that the design of the state machine favors specific distributions. Models of graphs of size up to 150 vertices are learned. Code and parameters are publicly available to reproduce our results.