We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct modeling of the global and local graph structure and helps to overcome the expressivity and mode collapse issues of one-shot graph generators. Our novel GAN, called SPECTRE, enables the one-shot generation of much larger graphs than previously possible with one-shot models. SPECTRE outperforms state-of-the-art deep autoregressive generators in terms of modeling fidelity, while also avoiding expensive sequential generation and dependence on node ordering. A case in point, in sizable synthetic and real-world graphs SPECTRE achieves a 4-to-170 fold improvement over the best competitor that does not overfit and is 23-to-30 times faster than autoregressive generators.

从光谱角度处理图形生成问题，通过生成图形拉普拉斯光谱的主导部分并构建一个与它们匹配的图形来解决图形生成的问题。这种新的 GAN 模型 SPECTRE 不仅实现了比传统模型更高效的生成，还提高了模型建模的保真度，并避免了昂贵的序列生成和依赖于节点排序的问题。

SPECTRE：光谱调节帮助克服一次性图生成器的表达能力限制