DeepGG: a Deep Graph Generator

TL;DR

DeepGG introduces an advanced deep state machine framework for learning graph distributions, enabling applications like drug discovery and network analysis, with demonstrated effectiveness on graphs up to 150 nodes.

Contribution

The paper presents a novel deep state machine-based approach for graph generation, incorporating graph and node embeddings as memory, and provides statistical analysis of learned distributions.

Findings

Successfully models graph distributions up to 150 nodes

Provides statistical tests for distribution learning quality

Code and parameters are publicly available

Abstract

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.