GRAN 优于 GraphRNN：用于图生成器的节点排序、基于核与图嵌入的指标
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
AI总结：
本文通过图不变量核指标及图嵌入空间中的流形、核指标评估 GraphRNN 与 GRAN，揭示节点排序影响，证明 GRAN 更优并给出实践指南。
摘要：
人们已经提出了各种各样的图生成模型。它们被用于药物发现、道路网络、神经架构搜索和程序合成。生成图存在一些理论挑战，例如同构表示——评估生成模型的性能好坏十分困难。应如何根据应用领域选择模型？我们广泛研究了图不变量分布上基于核的指标，以及图嵌入空间中基于流形和基于核的指标。基于流形的指标在嵌入空间中优于基于核的指标。我们使用这些指标比较两种著名的图生成模型 GraphRNN 和 GRAN，并揭示节点排序的影响。结果表明 GRAN 优于 GraphRNN；此外，我们提出的采用深度优先搜索排序的 GraphRNN 改进版本对小规模图是有效的。本文还提供了关于数据集选择和节点特征初始化良好实践的指南。我们的工作配有开源代码和可复现实验。
英文摘要：
A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating graphs has theoretical challenges, such as isomorphic representations -- evaluating how well a generative model performs is difficult. Which model to choose depending on the application domain? We extensively study kernel-based metrics on distributions of graph invariants and manifold-based and kernel-based metrics in graph embedding space. Manifold-based metrics outperform kernel-based metrics in embedding space. We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings. It shows the superiority of GRAN over GraphRNN - further, our proposed adaptation of GraphRNN with a depth-first search ordering is effective for small-sized graphs. A guideline on good practices regarding dataset selection and node feature initialization is provided. Our work is accompanied by open-source code and reproducible experiments.
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。
