Graph generation is integral to various engineering and scientific disciplines. Nevertheless, existing methodologies tend to overlook the generation of edge attributes. However, we identify critical applications where edge attributes are essential, making prior methods potentially unsuitable in such contexts. Moreover, while trivial adaptations are available, empirical investigations reveal their limited efficacy as they do not properly model the interplay among graph components. To address this, we propose a joint score-based model of nodes and edges for graph generation that considers all graph components. Our approach offers two key novelties: (i) node and edge attributes are combined in an attention module that generates samples based on the two ingredients; and (ii) node, edge and adjacency information are mutually dependent during the graph diffusion process. We evaluate our method on challenging benchmarks involving real-world and synthetic datasets in which edge features are crucial. Additionally, we introduce a new synthetic dataset that incorporates edge values. Furthermore, we propose a novel application that greatly benefits from the method due to its nature: the generation of traffic scenes represented as graphs. Our method outperforms other graph generation methods, demonstrating a significant advantage in edge-related measures.

翻译：图生成在工程和科学各个领域中都不可或缺。然而，现有方法往往忽视了边属性的生成。但我们发现，在关键应用场景中边属性至关重要，这使得先前的方法在此类场景中可能不适用。此外，尽管存在简单的适配方案，但实验研究表明，由于其未能适当建模图组件之间的相互作用，这些方案的效果有限。为此，我们提出了一种基于节点和边的联合得分模型，用于考虑所有图组件的图生成。我们的方法具有两大创新点：(i) 在注意力模块中融合节点和边属性，基于这两种要素生成样本；(ii) 在图扩散过程中，节点、边和邻接信息相互依赖。我们在涉及边缘特征至关重要的真实世界和合成数据集的具有挑战性的基准测试上评估了该方法。同时，我们引入了一个包含边值的新合成数据集。此外，我们提出了一种因方法特性而显著受益的新颖应用：生成为图的交通场景表示。我们的方法优于其他图生成方法，在边缘相关指标上显示出显著优势。