A Tunable Model for Graph Generation Using LSTM and Conditional VAE

TL;DR

This paper introduces a novel graph generation model combining LSTM and conditional VAE that can learn from data and generate graphs with tunable specific features, addressing limitations of previous models.

Contribution

The proposed model uniquely allows tuning of specific graph features during generation while learning structural properties from data.

Findings

Model can generate graphs with targeted features

Effective learning of structural graph features from data

Demonstrates tunability in graph generation

Abstract

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.