3 papers
cs.LG2026
GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets c…
cs.LG2026
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…
cs.LG2024
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…