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Self-supervised learning of graph neural networks: A unified review
Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …
A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …
many problems in computer vision, speech recognition, natural language processing, and …
Graphmae: Self-supervised masked graph autoencoders
Self-supervised learning (SSL) has been extensively explored in recent years. Particularly,
generative SSL has seen emerging success in natural language processing and other …
generative SSL has seen emerging success in natural language processing and other …
Graph self-supervised learning: A survey
Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …
works have focused on (semi-) supervised learning, resulting in shortcomings including …
Graphmae2: A decoding-enhanced masked self-supervised graph learner
Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …
offers great potential to address the fundamental challenge of label scarcity in real-world …
MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
To fully utilize the advances in omics technologies and achieve a more comprehensive
understanding of human diseases, novel computational methods are required for integrative …
understanding of human diseases, novel computational methods are required for integrative …
S2gae: Self-supervised graph autoencoders are generalizable learners with graph masking
Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …
Contrastive multi-view representation learning on graphs
We introduce a self-supervised approach for learning node and graph level representations
by contrasting structural views of graphs. We show that unlike visual representation learning …
by contrasting structural views of graphs. We show that unlike visual representation learning …
Multi-view contrastive graph clustering
With the explosive growth of information technology, multi-view graph data have become
increasingly prevalent and valuable. Most existing multi-view clustering techniques either …
increasingly prevalent and valuable. Most existing multi-view clustering techniques either …
Deep graph clustering via dual correlation reduction
Deep graph clustering, which aims to reveal the underlying graph structure and divide the
nodes into different groups, has attracted intensive attention in recent years. However, we …
nodes into different groups, has attracted intensive attention in recent years. However, we …