2 citations · 4 across the 15 of their papers we have counts for
7 papers · 1 filter
Anchor-guided Hypergraph Condensation with Dual-level Discrimination
Fan Li, Xiaoyang Wang, Chen Chen +1
The increasing prevalence of large-scale hypergraphs poses significant computational challenges for hypergraph neural network (HNN) training. To address this, hypergraph condensati…
Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
Zian Zhai, Fan Li, Xingyu Tan +2
Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data. However, a fundamental challen…
SGPT: Few-Shot Prompt Tuning for Signed Graphs
Zian Zhai, Sima Qing, Xiaoyang Wang +1
Signed Graph Neural Networks (SGNNs) are effective in learning expressive representations for signed graphs but typically require substantial task-specific labels, limiting their a…
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning
Guangxin Su, Yifan Zhu, Wenjie Zhang +2
Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art meth…
TCGU: Data-centric Graph Unlearning based on Transferable Condensation
Fan Li, Xiaoyang Wang, Dawei Cheng +3
With growing demands for data privacy and model robustness, graph unlearning (GU), which erases the influence of specific data on trained GNN models, has gained significant attenti…
RIDA: A Robust Attack Framework on Incomplete Graphs
Jianke Yu, Hanchen Wang, Chen Chen +5
Graph Neural Networks (GNNs) are vital in data science but are increasingly susceptible to adversarial attacks. To help researchers develop more robust GNN models, it's essential t…
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Fan Li, Xiaoyang Wang, Dawei Cheng +3
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly…