Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering. Graph AE, VAE and most of their extensions rely on graph convolutional networks (GCN) to learn vector space representations of nodes. In this paper, we propose to replace the GCN encoder by a simple linear model w.r.t. the adjacency matrix of the graph. For the two aforementioned tasks, we empirically show that this approach consistently reaches competitive performances w.r.t. GCN-based models for numerous real-world graphs, including the widely used Cora, Citeseer and Pubmed citation networks that became the de facto benchmark datasets for evaluating graph AE and VAE. This result questions the relevance of repeatedly using these three datasets to compare complex graph AE and VAE models. It also emphasizes the effectiveness of simple node encoding schemes for many real-world applications.

本文提出一种替代图卷积网络 (GCN) 编码器的简单线性模型来学习节点的向量表示，并通过实验证明在多个现实世界的图中，如 Cora、Citeseer 和 Pubmed 引文网络上，其表现与基于 GCN 的模型相当，这结果对于评估复杂图自动编码器和变分自动编码器的实际实用价值具有重要意义。

保持简单：无需图卷积网络的图自编码器