3 citations · 4 across the 3 of their papers we have counts for
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cs.LG2023★ 1 cited
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Gehang Zhang, Bowen Yu, Jiangxia Cao +4
Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different ``views'' of the same graph in order to learn node/gr…
cs.LG2023★ 3 cited
State of the Art and Potentialities of Graph-level Learning
Zhenyu Yang, Ge Zhang, Jia Wu +9
Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as inp…
cs.LG2022
Graph-level Neural Networks: Current Progress and Future Directions
Ge Zhang, Jia Wu, Jian Yang +6
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of…