论文标题
离题：分离deo的扩散量
DiGress: Discrete Denoising diffusion for graph generation
论文作者
论文摘要
这项工作介绍了离题，这是一种用于生成具有分类节点和边缘属性图的图形的离散denoising扩散模型。我们的模型利用一个离散的扩散过程，该过程通过添加或删除边缘并更改类别的过程逐步编辑噪声图。训练了图形变压器网络来恢复此过程，将图形学习的问题简化为节点和边缘分类任务序列。我们通过引入马尔可夫噪声模型来进一步提高样品质量，该模型在扩散过程中保留节点和边缘类型的边际分布，并通过结合辅助图理论特征。还提出了用于在图级特征上生成的过程。离题在分子和非分子数据集上实现了最新的性能，在平面图数据集上，有效性提高了3倍。它也是第一个扩展到含有130万个药物样分子的大型鳄梨调子数据集的模型，而无需使用分子特异性表示。
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or removing edges and changing the categories. A graph transformer network is trained to revert this process, simplifying the problem of distribution learning over graphs into a sequence of node and edge classification tasks. We further improve sample quality by introducing a Markovian noise model that preserves the marginal distribution of node and edge types during diffusion, and by incorporating auxiliary graph-theoretic features. A procedure for conditioning the generation on graph-level features is also proposed. DiGress achieves state-of-the-art performance on molecular and non-molecular datasets, with up to 3x validity improvement on a planar graph dataset. It is also the first model to scale to the large GuacaMol dataset containing 1.3M drug-like molecules without the use of molecule-specific representations.
