1. Edge-based sequential graph generation with recurrent neural networks.
- Author
-
Bacciu, Davide, Micheli, Alessio, and Podda, Marco
- Subjects
- *
RECURRENT neural networks , *OPEN learning , *MACHINE learning - Abstract
• We propose to generate graphs node by node in a sequential fashion. • Generation is carried out by two Recurrent Neural Networks that sample the nodes attached to an edge sequentially. • Our model is capable of producing novel and unique graphs that retain structural features of those in the training set. Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF