论文标题
自动编码图
Auto-decoding Graphs
论文作者
论文摘要
我们提出了一种从经验指定的分布中综合新图形结构的方法。生成模型是一种自动模型，可以学会从潜在代码合成图形。图形合成模型是在潜在代码上具有经验分布共同学习的。使用经过训练以识别可能的连通性模式的自我发场模块合成图形。基于图的归一化流量用于从自动码头学到的分布中采样潜在代码。最终的模型结合了准确性和可扩展性。在大图的基准数据集上，所呈现的模型的表现优于最高准确性的1.5倍，并且在推理过程中至少三个不同的图统计量的平均准确性和平均等级为2倍。
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.
