"Contrastive Learning Augmented Graph Auto-Encoder" by Shuaishuai Zu, Chuyu Wang et al.
Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, leading to unsatisfactory generalization performance. In order to preserve the maximal information, graph contrastive learning (GCL) has become a prominent technique for learning discriminative embeddings. However, in contrast with graph-level embedd...
Contrastive Variational Graph Auto Encoder Contrastive Learning Istribution Dependent Regularization Raph Auto Encoder Runcated Triplet Loss
Source: uow.edu.au