Exploring the graph latent structures has not garnered much attention in the graph generative research field. Yet, exploiting the latent space is as crucial as working on the data space for discrete data such as graphs. However, previous methods either failed to preserve the permutation symmetry of graphs or lacked an effective approaches to model appropriately within the latent space. To mitigate those issues, we propose a simple, yet effective discrete latent graph diffusion generative model. Our model, namely GLAD, not only overcomes the drawbacks of existing latent approaches, but also alleviates inherent issues present in diffusion methods applied on the graph space. We validate our generative model on the molecular benchmark datasets, on which it demonstrates competitive performance compared with the state-of-the-art baselines.

翻译：在图生成研究领域，探索图的潜在结构尚未获得广泛关注。然而，对于图这类离散数据而言，利用潜在空间与在数据空间中进行操作同等重要。然而，先前的方法要么未能保持图的置换对称性，要么缺乏在潜在空间内进行适当建模的有效途径。为缓解这些问题，我们提出了一种简单而有效的离散潜在图扩散生成模型。我们的模型（称为GLAD）不仅克服了现有潜在方法的缺点，还缓解了扩散方法应用于图空间时存在的固有问题。我们在分子基准数据集上验证了生成模型，结果表明其性能与最先进的基线模型相比具有竞争力。