论文标题
图形生成的不致密层
An Unpooling Layer for Graph Generation
论文作者
论文摘要
我们提出了一个新颖且可训练的图形未冷却层，以进行有效的图生成。给定具有功能的图形，未冷却层扩大了此图，并了解了其所需的新结构和功能。由于该不化层是可以训练的，因此可以将其应用于变异自动编码器的解码器或生成对抗网络（GAN）的生成器中。我们证明了未冷却的图保持连接，并且任何连接的图形都可以从3节点图中顺序不明。我们在GAN发电机中应用不化层。由于研究最多的实例是分子产生，因此我们在这种情况下测试了我们的想法。使用QM9和锌数据集，我们证明了通过使用未解决层而不是基于邻接的matrix方法获得的改进。
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.
