论文标题
幽灵：光谱调节有助于克服一击图发生器的表达限制
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
论文作者
论文摘要
我们通过首先生成图形laplacian光谱的主要部分，然后构建匹配这些特征值和特征向量的图形来解决图形生成问题。光谱调节允许直接建模全局和本地图结构，并有助于克服一击图生成器的表达性和模式崩溃问题。我们的新颖的甘恩（Spectre）称为Specter，可以使用一声模型来产生比以前可能更大的图。 Spectre的表现优于最先进的深度自动回归发生器在建模忠诚方面，同时避免了昂贵的顺序产生和对节点排序的依赖。一个很好的例子，在相当大的合成图和现实图形中，Specter的幽灵比最佳竞争者的提高了4至170倍，该竞争对手不合适，并且比自回旋发电机快23至30倍。
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.
