Graph generation generally aims to create new graphs that closely align with a specific graph distribution. Existing works often implicitly capture this distribution through the optimization of generators, potentially overlooking the intricacies of the distribution itself. Furthermore, these approaches generally neglect the insights offered by the learned distribution for graph generation. In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to guide the generation process. We first perform self-conditioned modeling to capture the graph distributions by transforming each graph sample into a low-dimensional representation and optimizing a representation generator to create new representations reflective of the learned distribution. Subsequently, we leverage these bootstrapped representations as self-conditioned guidance for the generation process, thereby facilitating the generation of graphs that more accurately reflect the learned distributions. We conduct extensive experiments on generic and molecular graph datasets across various fields. Our framework demonstrates superior performance over existing state-of-the-art graph generation methods in terms of graph quality and fidelity to training data.

翻译：摘要：图生成通常旨在创建与特定图分布高度一致的新图。现有工作往往通过优化生成器隐式捕获此分布，可能忽略了分布本身的复杂性。此外，这些方法通常忽视所学分布对图生成的指导价值。与此相反，本文提出一种新颖的自条件图生成框架，旨在显式建模图分布并利用这些分布指导生成过程。我们首先通过将每个图样本转化为低维表示并优化一个表示生成器以创建反映所学分布的新表示，从而执行自条件建模以捕获图分布。随后，我们利用这些自举表示作为生成过程的自条件引导，从而促进生成更准确反映所学分布的图。我们在跨领域的通用图数据集与分子图数据集上进行了广泛实验。相较于现有最先进的图生成方法，我们的框架在图质量和与训练数据的保真度方面均展现出更优性能。