A Tunable Model for Graph Generation Using LSTM and Conditional VAE
This work addresses the need for tunable graph generation in graph applications, but it is incremental as it builds on existing machine learning methods without introducing a new paradigm.
The paper tackles the problem of generating graphs with specific, tunable features by proposing a generative model that learns structural features from data, and they confirm its ability to generate graphs with desired features using a dataset from a stochastic model.
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.