Abstract: Contrastive learning and generative methodologies in graph self-supervised learning offer efficient strategies for managing graph data with scarce labels. Among these techniques, the masked ...

To tackle the above issue, we present a multilevel contrastive graph masked autoencoder (MCGMAE) for unsupervised GSL. Specifically, we first introduce a graph masked autoencoder with the dual feature ...

Researchers introduced X-CLR, a novel contrastive learning method using graph-based sample relationships. This approach ...

By leveraging graph message-passing layers, graph feature augmentation and contrastive learning, the proposed CGAE embeds highly discriminative latent embeddings by reconstructing graph features w.r.t ...

Existing GNN and contrastive-learning-based recommendation models learn user and item representations in a symmetrical way and utilize social information and contrastive learning in a complex manner.

Keywords: microbe-disease associations, graph convolutional network, graph attention mechanism, contrastive learning, gut microbial metagenomics. Citation: Jiang C, Feng J, Shan B, Chen Q, Yang J, ...