GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features

Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces real-world graphs has gradually attracted the attention of many researchers. Several generative models that utilize modern machine learning technologies have been proposed, though a conditional generation of general graphs is less explored in the field. In this paper, we propose a generative model that allows us to tune a value of a global-level structural feature as a condition. Our model called GraphTune enables to tune a value of any structural feature of generated graphs using Long Short Term Memory (LSTM) and Conditional Variational AutoEncoder (CVAE). We performed comparative evaluations of GraphTune and conventional models with a real graph dataset. The evaluations show that GraphTune enables to clearly tune a value of a global-level structural feature compared to the conventional models.

A Tunable Model for Graph Generation Using LSTM and Conditional VAE

With the development of graph applications, generative models for graphs have been more crucial. Classically, stochastic models that generate graphs with a pre-defined probability of edges and nodes have been studied. Recently, some models that reproduce the structural features of graphs by learning from actual graph data using machine learning have been studied. However, in these conventional studies based on machine learning, structural features of graphs can be learned from data, but it is not possible to tune features and generate graphs with specific features. In this paper, we propose a generative model that can tune specific features, while learning structural features of a graph from data. With a dataset of graphs with various features generated by a stochastic model, we confirm that our model can generate a graph with specific features.