做图生成研究的团队终于有了一个能兼顾大小图质量的统一框架——FLAGG 让单次模型也能自回归生成，实验效果全面超越基线，值得关注。
FLAGG 提出了一种灵活的自回归图生成框架，通过将单次生成模型应用于图的部分顺序生成，解决了单次模型在大图上表现差、顺序模型在小图上效果不佳的难题。该框架通过随机节点移除过程定义生成策略，并由插入模型学习逆向操作，从而在多种图数据集上实现了优于纯单次和纯顺序模型的采样质量。FLAGG 可适配任意单次模型，使其具备自回归能力，为图生成任务提供了更通用的解决方案。
FLAGG: Flexible Autoregressive Graph Generation
The Deep Graph Generation's panorama spans two extremes: one-shot and sequential models. The former generates nodes and edges jointly, while the latter samples them autoregressively. Each method performs better in different graph domains depending on size and topology, but neither is applicable to all graph categories. For instance, one-shot methods struggle with generating large graphs, while sequential methods underperform on smaller graphs. A possible way to overcome these limitations is to flexibly combine the two methods in a unique system. In this work, we propose the FLAGG (Flexible Autoregressive Graph Generation) framework, which sequentially generates portions of graphs with one-shot models. FLAGG can apply any one-shot model to make it autoregressive, allowing flexibility in choosing the sequential policy. This policy is specified through a stochastic node removal process, which an Insertion Model learns to reverse. We evaluate FLAGG with the DiGress one-shot model on several data sets of different graph sizes and domains. We show that the approach outperforms both one-shot and autoregressive baselines in terms of sampling quality.