Abstract
Self-supervised auto-encoders have emerged as a successful framework for representation learning in computer vision and natural language processing in recent years, However, their application to graph data has been met with limited performance due to the non-Euclidean and complex structure of graphs in comparison to images or text, as well as the limitations of conventional auto-encoder architectures. In this paper, we investigate factors impacting the performance of auto-encoders on graph data and propose a novel auto-encoder model for graph representation learning. Our model incorporates a hierarchical adaptive masking mechanism to incrementally increase the difficulty of training in order to mimic the process of human cognitive learning, and a trainable corruption scheme to enhance the robustness of learned representations. Through extensive experimentation on ten benchmark datasets, we demonstrate the superiority of our proposed method over state-of-the-art graph representation learning models.
Abstract (translated)
自监督自编码器在计算机视觉和自然语言处理中已经成为一种成功的框架,近年来,但对于图形数据的应用,其性能受到了限制,这是因为图形相对于图像或文本具有非欧几何和复杂结构,并且传统自编码器架构的局限性也仍然存在。在本文中,我们探讨了影响自编码器对图形数据性能的因素,并提出了一种新的自编码器模型,用于图形表示学习。我们的模型采用了Hierarchical Adaptive Masking机制,逐步增加训练的难度,以模拟人类认知学习的过程,并使用可训练的腐败策略来增强学习表示的鲁棒性。通过在十个基准数据集上进行广泛的实验,我们证明了我们提出的方法比当前图形表示学习模型更有效。
URL
https://arxiv.org/abs/2301.12063