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.

翻译：我们从光谱角度处理图形生成问题,先是生成Laplacian 光谱中的主导部分,然后绘制一个匹配这些等离子值和成形器的图表。 光谱调节使全球和本地图形结构能够直接建模, 有助于克服一发图形生成器的表达性和模式崩溃问题。 我们的新型GAN, 名为 SPECTRE, 能够以一发模型生成比以前可能生成的更大得多的一发图形。 SPECT在模拟真实性方面优于最先进的深自闭式生成器, 同时避免昂贵的相继生成和对节点订购的依赖。 典型的例子, 即可变合成和真实世界图形 SPECTRE 能够比最佳的兼容器实现4- 170 倍的改进, 它不会过度使用,比自动递增的发电机快23- 30 倍 。