1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification
Jiawei Sun, Hongkang Li, Meng Wang
Jumping connections enable Graph Convolutional Networks (GCNs) to overcome over-smoothing, while graph sparsification reduces computational demands by selecting a sub-matrix of the…
LocalGCL: Local-aware Contrastive Learning for Graphs
Haojun Jiang, Jiawei Sun, Jie Li +1
Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-cons…
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention
Junchi Yan, Fangyu Ding, Jiawei Sun +3
Graph invariant learning (GIL) seeks invariant relations between graphs and labels under distribution shifts. Recent works try to extract an invariant subgraph to improve out-of-di…
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Jiawei Sun, Ruoxin Chen, Jie Li +3
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…