边缘上的生命复苏：基于联合分数的丰富边属性图生成
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes
AI总结：
本文针对现有图生成方法忽视边属性的问题，提出节点与边联合的分数生成模型，通过注意力模块和相互依赖的扩散过程生成具有丰富边属性的图，并在交通场景生成等任务中取得显著优势。
摘要：
图生成是各种工程和科学学科中不可或缺的一部分。然而，现有方法往往忽略了边属性的生成。但我们识别出一些边属性至关重要的关键应用，这使得先前的方法在此类场景中可能并不适用。此外，尽管存在一些简单的适应性修改，实证研究表明其效果有限，因为它们未能恰当地建模图各组成部分之间的相互作用。为解决这一问题，我们提出了一种面向图生成的节点与边的联合分数模型，该模型考虑了所有图组成部分。我们的方法具有三项关键创新：（1）节点和边属性在注意力模块中结合，该模块基于这两种成分生成样本；（2）在图扩散过程中，节点、边和邻接信息相互依赖；（3）该框架能够生成沿边具有丰富属性的图，为生成任务提供了比现有工作更具表达力的形式化方法。我们在具有挑战性的基准上评估了我们的方法，这些基准涉及边特征至关重要的真实世界和合成数据集。此外，我们引入了一个包含边值的新合成数据集。进一步地，我们提出了一种因其本质特性而极大受益于该方法的新应用：以图表示形式生成交通场景。我们的方法优于其他图生成方法，在与边相关的度量上展现出显著优势。
英文摘要：
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 three key novelties: \textbf{(1)} node and edge attributes are combined in an attention module that generates samples based on the two ingredients, \textbf{(2)} node, edge and adjacency information are mutually dependent during the graph diffusion process, and \textbf{(3)} the framework enables the generation of graphs with rich attributes along the edges, providing a more expressive formulation for generative tasks than existing works. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
