论文标题
图形卷积生成
Graph Deconvolutional Generation
论文作者
论文摘要
图是一项非常重要的任务，因为在科学和工程的不同领域都可以找到图形。在这项工作中，我们着重于现代的ERDOS-RENYI随机图模型：图形变异自动编码器（GVAE）。该模型假设边缘和节点是独立的，以便使用多层感知器解码器一次生成整个图。由于这些假设，GVAE难以匹配训练分布并依赖昂贵的图形匹配程序。我们通过构建信息将神经网络传递到GVAE的编码器和解码器中来改善这类模型。我们展示了产生小有机分子的特定任务的模型
Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering. In this work, we focus on the modern equivalent of the Erdos-Renyi random graph model: the graph variational autoencoder (GVAE). This model assumes edges and nodes are independent in order to generate entire graphs at a time using a multi-layer perceptron decoder. As a result of these assumptions, GVAE has difficulty matching the training distribution and relies on an expensive graph matching procedure. We improve this class of models by building a message passing neural network into GVAE's encoder and decoder. We demonstrate our model on the specific task of generating small organic molecules
