Recently developed graph contrastive learning (GCL) approaches compare t...
Graph neural architecture search (NAS) has gained popularity in automati...
Graphs have a superior ability to represent relational data, like chemic...
Self-supervised learning (especially contrastive learning) methods on
he...
Graph-structured data consisting of objects (i.e., nodes) and relationsh...
Graph neural networks (GNNs) offer promising learning methods for
graph-...
Graph Convolutional Networks (GCNs) are typically studied through the le...
Graph representation learning plays a vital role in processing
graph-str...
User cold-start recommendation is a long-standing challenge for recommen...
While numerous approaches have been developed to embed graphs into eithe...
As communities represent similar opinions, similar functions, similar
pu...
Predicting pairs of anchor users plays an important role in the cross-ne...
Graph Neural Networks (GNNs) have been popularly used for analyzing
non-...