Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.
翻译:图形自动编码器(AE)和变式自动编码器(VAE)最近作为强大的节点嵌入方法出现。特别是,图AE和VAE成功地被利用解决了具有挑战性的链接预测问题,目的是了解图AE和VAE中的一些节点是否由未观测的边缘连接在一起。然而,这些模型侧重于非定向图形,因此忽略了链接的潜在方向,而这种方向正在限制许多实际应用。在本文中,我们扩展了图AE和VAE框架,以解决定向图形中的链接预测。我们提出了一个新的重力驱动解码器方案,能够有效地从节点嵌中重建定向图形。我们用实验性评估了我们关于三种不同定向链接预测任务的方法,而标准图表AE和VAE的运行情况很差。我们在三个真实世界图上取得了竞争性结果,超过了几个流行的基准。