结合路径掩蔽和双解码器的图自编码器框架

Graph Autoencoder Framework Combining Path Masking and Dual Decoder

图自编码器作为一种自监督学习方法,在图神经网络领域得到广泛应用。然而,最近的研究表明,现有的图自编码器通常重构整个图结构,容易产生过拟合数据。此外,这些方法过度强调邻居信息而忽略了结构信息,导致在节点分类任务中表现不佳。针对以上问题,提出了基于路径掩蔽和双解码器的图自编码器框架,用于图表示学习。通过路径掩蔽方法扰动输入图,避免产生过拟合数据。将图神经网络作为编码器,在剩余的图结构上进行消息传递,提高了对图数据的学习能力。提出双解码器对掩蔽边重构,既包含了邻居信息又捕获了结构信息。模型在5个公开的图数据集上进行了实验,并与当前具有代表性的图表示学习方法进行了对比。实验结果表明,提出的方法在5个数据集上均取得了相似或更好的效果,并且在链接预测和节点分类任务上优于基线方法。

Graph autoencoder,as a self-supervised learning method,has been widely applied in the field of graph neural networks.However,recent studies have shown that existing graph autoencoders often reconstruct the entire graph struc-ture,leading to overfitting issues.Additionally,these methods tend to overemphasize neighbor information while neglecting structural information,resulting in poor performance on node classification tasks.To address these issues,a graph autoen-coder framework based on path masking and dual decoders is proposed for graph representation learning.Firstly,the input graph is perturbed using path masking to avoid generating overfitting data.Secondly,a graph neural network is employed as the encoder to perform message passing on the remaining graph structure,enhancing the learning capability for graph data.Finally,dual decoders are introduced to reconstruct the masked edges,capturing both neighbor information and struc-tural information.The model is evaluated on five publicly available graph datasets and compared with state-of-the-art graph representation learning methods.Experimental results demonstrate that the proposed approach achieves similar or better performance on all five datasets and outperforms baseline methods in link prediction and node classification tasks.