Unsupervised graph representation learning aims to distill various graph
information into a downstream task-agnostic dense vector embedding. However,
existing graph representation learning approaches are designed mainly under the
node homophily assumption: connected nodes tend to have similar labels and
optimize performance on node-centric downstream tasks. Their design is
apparently against the task-agnostic principle and generally suffers poor
performance in tasks, e.g., edge classification, that demands feature signals
beyond the node-view and homophily assumption. To condense different feature
signals into the embeddings, this paper proposes PairE, a novel unsupervised
graph embedding method using two paired nodes as the basic unit of embedding to
retain the high-frequency signals between nodes to support node-related and
edge-related tasks. Accordingly, a multi-self-supervised autoencoder is
designed to fulfill two pretext tasks: one retains the high-frequency signal
better, and another enhances the representation of commonality. Our extensive
experiments on a diversity of benchmark datasets clearly show that PairE
outperforms the unsupervised state-of-the-art baselines, with up to 101.1\%
relative improvement on the edge classification tasks that rely on both the
high and low-frequency signals in the pair and up to 82.5\% relative
performance gain on the node classification tasks.

本文提出了 PairE，一种新的无监督图嵌入方法，使用两个匹配的节点作为嵌入的基本单元以捕捉节点关系和边缘信号，并利用多自监督自编码器完成两个预处理任务以提高性能，实验证明在边缘分类和节点分类任务上有相对较高的性能提升。