9 citations · 17 across the 11 of their papers we have counts for
5 papers · 1 filter
DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts
Zelin Yao, Chuang Liu, Xianke Meng +4
Graph neural networks (GNNs) are gaining popularity for processing graph-structured data. In real-world scenarios, graph data within the same dataset can vary significantly in scal…
Dual-perspective Cross Contrastive Learning in Graph Transformers
Zelin Yao, Chuang Liu, Xueqi Ma +5
Graph contrastive learning (GCL) is a popular method for leaning graph representations by maximizing the consistency of features across augmented views. Traditional GCL methods uti…
Hi-GMAE: Hierarchical Graph Masked Autoencoders
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node…
Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders
Chuang Liu, Yuyao Wang, Yibing Zhan +4
Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a strai…
Gradformer: Graph Transformer with Exponential Decay
Chuang Liu, Zelin Yao, Yibing Zhan +3
Graph Transformers (GTs) have demonstrated their advantages across a wide range of tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases, parti…