As one of the important models in the field of deep learning,graph autoencoder(GAE)has re-ceived extensive attention in recent years.However,GAE tends to overemphasize proximity infor-mation at the expense of structural information of the graph,making it unsuitable for downstream tasks other than link prediction.In view of the problems existing in traditional GAE,researchers introduce mask autoencoder(MAE)into the generative self-supervised learning model represented by GAE.Based on this,this article proposed the mask graph autoencoder(MaskGAE),which uses the mask graph model(MGM)as an agent task to mask part of the edges,and tries to reconstruct the missing part with a partially visible,unmasked graph structure.In this paper,the node classifi-cation accuracy of the MaskGAE model is improved from 84.05％to 84.55％and the accuracy is increased by 0.5％by adjusting parameters on the Cora dataset.
Autoencoderself-supervised learningMasked graph modelinggraph-structured data
万方数据
维普
