Self-supervised graph representation learning has been widely used in many intelligent applications since labeled information can hardly be found in these data environments. Currently, masking and reconstruction-based (MR-based) methods lead the state-of-the-art records in the self-supervised graph representation field. However, existing MR-based methods did not fully consider both the deep-level node and structure information which might decrease the final performance of the graph representation. To this end, this paper proposes a node and edge dual-masked self-supervised graph representation model to consider both node and structure information. First, a dual masking model is proposed to perform node masking and edge masking on the original graph at the same time to generate two masking graphs. Second, a graph encoder is designed to encode the two generated masking graphs. Then, two reconstruction decoders are designed to reconstruct the nodes and edges according to the masking graphs. At last, the reconstructed nodes and edges are compared with the original nodes and edges to calculate the loss values without using the labeled information. The proposed method is validated on a total of 14 datasets for graph node classification tasks and graph classification tasks. The experimental results show that the method is effective in self-supervised graph representation. The code is available at: https://github.com/TangPeng0627/Node-and-Edge-Dual-Mask.

自监督图表示学习已广泛应用于许多智能应用中，因为在这些数据环境中很难找到标记信息。目前，基于掩蔽和重建（基于MR）的方法引领着自监督图表示领域的最先进记录。然而，现有的基于MR的方法没有充分考虑深层节点和结构信息，这可能会降低图表示的最终性能。为此，本文提出了一种节点和边双掩码自监督图表示模型，以同时考虑节点和结构信息。首先，提出双掩蔽模型，对原始图同时进行节点掩蔽和边缘掩蔽，生成两个掩蔽图。其次，设计图编码器来对两个生成的掩蔽图进行编码。然后，设计两个重建解码器根据掩蔽图重建节点和边。最后，将重建的节点和边与原始节点和边进行比较，以计算损失值，而不使用标记信息。该方法在总共 14 个图节点分类任务和图分类任务数据集上进行了验证。实验结果表明该方法在自监督图表示方面是有效的。代码位于：https://github.com/TangPeng0627/Node-and-Edge-Dual-Mask。