2022/06/04 by Yinglong Guo, Dongmian Zou, Guo, Yinglong +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · #Bioinformatics and Genomic Networks #Cell Image Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2206.01874
openalex publication_date 2022/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a novel and trainable graph unpooling layer for effective graph generation. Given a graph with features, the unpooling layer enlarges this graph and learns its desired new structure and features. Since this unpooling layer is trainable, it can be applied to graph generation either in the decoder of a variational autoencoder or in the generator of a generative adversarial network (GAN). We prove that the unpooled graph remains connected and any connected graph can be sequentially unpooled from a 3-nodes graph. We apply the unpooling layer within the GAN generator. Since the most studied instance of graph generation is molecular generation, we test our ideas in this context. Using the QM9 and ZINC datasets, we demonstrate the improvement obtained by using the unpooling layer instead of an adjacency-matrix-based approach.