自动解码图（CS ML）---蔡秋纯

我们提出了一种根据经验指定的分布合成新图结构的方法。 生成模型是一种自动解码器，可学习从潜在代码合成图。 结合潜在代码的经验分布来学习图综合模型。 使用经过训练以识别可能的连通性模式的自我注意模块来合成图。 基于图的归一化流程用于从自动解码器学习的分布中采样潜在代码。 生成的模型结合了准确性和可伸缩性。 在大型图的基准数据集上，所提出的模型在至少三个不同的图统计量上的平均准确度和平均等级比现有技术高出1.5倍，推理过程中的速度提高了2倍。

原文标题：Auto-decoding Graphs

原文：We present an approach to synthesizing new graph structures from empirically specified distributions. The generative model is an auto-decoder that learns to synthesize graphs from latent codes. The graph synthesis model is learned jointly with an empirical distribution over the latent codes. Graphs are synthesized using self-attention modules that are trained to identify likely connectivity patterns. Graph-based normalizing flows are used to sample latent codes from the distribution learned by the auto-decoder. The resulting model combines accuracy and scalability. On benchmark datasets of large graphs, the presented model outperforms the state of the art by a factor of 1.5 in mean accuracy and average rank across at least three different graph statistics, with a 2x speedup during inference.

原文作者：Sohil Atul Shah, Vladlen Koltun

原文地址：https://arxiv.org/abs/2006.028