Graph autoencoders (GAEs), as a kind of generative self-supervised learning approach, have shown great potential in recent years. GAEs typically rely on distance-based criteria, such as mean-square-error (MSE), to reconstruct the input graph. However, relying solely on a single reconstruction criterion may lead to a loss of distinctiveness in the reconstructed graph, causing nodes to collapse into similar representations and resulting in sub-optimal performance. To address this issue, we have developed a simple yet effective strategy to preserve the necessary distinctness in the reconstructed graph. Inspired by the knowledge distillation technique, we found that the dual encoder-decoder architecture of GAEs can be viewed as a teacher-student relationship. Therefore, we propose transferring the knowledge of distinctness from the raw graph to the reconstructed graph, achieved through a simple KL constraint. Specifically, we compute pairwise node similarity scores in the raw graph and reconstructed graph. During the training process, the KL constraint is optimized alongside the reconstruction criterion. We conducted extensive experiments across three types of graph tasks, demonstrating the effectiveness and generality of our strategy. This indicates that the proposed approach can be employed as a plug-and-play method to avoid vague reconstructions and enhance overall performance.

翻译：图自编码器（GAEs）作为一种生成式自监督学习方法，近年来展现出巨大潜力。GAEs通常依赖基于距离的准则（例如均方误差）来重构输入图。然而，仅依赖单一重构准则可能导致重构图中节点区分度的丧失，致使节点坍缩为相似表示，从而产生次优性能。为解决这一问题，我们开发了一种简单而有效的策略来保持重构图中必要的区分度。受知识蒸馏技术启发，我们发现GAEs的双重编码器-解码器架构可视为一种师生关系。因此，我们提出将原始图中的区分度知识迁移至重构图，这一目标通过简单的KL约束实现。具体而言，我们分别计算原始图与重构图中的成对节点相似度分数。在训练过程中，KL约束与重构准则共同优化。我们在三类图任务上进行了广泛实验，证明了该策略的有效性与普适性。这表明所提方法可作为即插即用的技术方案，用于避免模糊重构并提升整体性能。