论文标题
FastGae：可扩展的图形自动编码器，具有随机子图解码
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
论文作者
论文摘要
Graph AutoCododers（AE）和变化自动编码器（VAE）是强大的节点嵌入方法，但遇到了可扩展性问题。在本文中，我们介绍了FastGae，这是一个将AE和VAE缩放为数百万节点和边缘的大图的一般框架。我们的策略基于有效的随机子图解码方案，在保留甚至改善性能的同时，大大加快了图AE和VAE的训练。我们证明了快速对各种现实图表的有效性，超过了几种现有的方法来扩展AE和VAE的方法。
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of nodes and edges. Our strategy, based on an effective stochastic subgraph decoding scheme, significantly speeds up the training of graph AE and VAE while preserving or even improving performances. We demonstrate the effectiveness of FastGAE on various real-world graphs, outperforming the few existing approaches to scale graph AE and VAE by a wide margin.
