Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood cannot be easily extended. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct personalized embedding propagation (PEP) and its approximation, PEP$_\text{A}$. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification on multiple graphs in the most thorough study done so far for GCN-like models.

本文通过引入PageRank的个性化传播机制，构建了PPNP和其快速逼近版本APPNP两种新的方法，其训练速度相当或更快，参数数量相当或更少，并且与任何神经网络结合使用， 用于半监督分类，在最彻底的GCN-like模型的研究中，展示了该模型胜过几种近期提出的方法。

预测后传播：图神经网络遇见个性化PageRank