1 citations · 1 across the 4 of their papers we have counts for
8 papers
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…
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…
Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning
Tianle Xia, Liang Ding, Guojia Wan +3
Answering complex queries over incomplete knowledge graphs (KGs) is a challenging job. Most previous works have focused on learning entity/relation embeddings and simulating first-…
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…
Exploring Sparsity in Graph Transformers
Chuang Liu, Yibing Zhan, Xueqi Ma +5
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, espe…