Nodes vj and vk are selected from the original graph Gi while nodes uj and uk are sampled from a feature-augmented graph Gf,i at the same position.</p
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (G...
Graph-level representations are critical in various real-world applications, such as predicting the ...
Unsupervised contrastive learning has recently become increasingly popular due to its amazing perfor...
(Gf,i, Gs,i) is a positive pair originated from a graph Gi, and (Gf,i, Gs,i′) for i ≠ i′ are negativ...
TAG performs node-level and graph-level contrastive learning on the feature-augmented graph Gf,i and...
TAG first augments all graphs in a training set , and then performs node-level and graph-level contr...
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation lear...
Graph is a type of structured data to describe the multiple objects as well as their relationships, ...
Contrastive learning is an effective unsupervised method in graph representation learning. Recently,...
Graphs are powerful representations for relations among objects, which have attracted plenty of atte...
Benefiting from the intrinsic supervision information exploitation capability, contrastive learning ...
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance ...
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance ...
Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representati...
Graph Contrastive Learning (GCL) has drawn much research interest due to its strong ability to captu...
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (G...
Graph-level representations are critical in various real-world applications, such as predicting the ...
Unsupervised contrastive learning has recently become increasingly popular due to its amazing perfor...
(Gf,i, Gs,i) is a positive pair originated from a graph Gi, and (Gf,i, Gs,i′) for i ≠ i′ are negativ...
TAG performs node-level and graph-level contrastive learning on the feature-augmented graph Gf,i and...
TAG first augments all graphs in a training set , and then performs node-level and graph-level contr...
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation lear...
Graph is a type of structured data to describe the multiple objects as well as their relationships, ...
Contrastive learning is an effective unsupervised method in graph representation learning. Recently,...
Graphs are powerful representations for relations among objects, which have attracted plenty of atte...
Benefiting from the intrinsic supervision information exploitation capability, contrastive learning ...
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance ...
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance ...
Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representati...
Graph Contrastive Learning (GCL) has drawn much research interest due to its strong ability to captu...
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (G...
Graph-level representations are critical in various real-world applications, such as predicting the ...
Unsupervised contrastive learning has recently become increasingly popular due to its amazing perfor...
DOI
Add to ORKG