44 citations · 113 across the 21 of their papers we have counts for
6 papers · 1 filter
GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
Rui Lv, Zaixi Zhang, Kai Zhang +6
Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in t…
Towards Few-shot Self-explaining Graph Neural Networks
Jingyu Peng, Qi Liu, Linan Yue +3
Recent advancements in Graph Neural Networks (GNNs) have spurred an upsurge of research dedicated to enhancing the explainability of GNNs, particularly in critical domains such as…
FedGT: Federated Node Classification with Scalable Graph Transformer
Zaixi Zhang, Qingyong Hu, Yang Yu +2
Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local…
Hierarchical Graph Transformer with Adaptive Node Sampling
Zaixi Zhang, Qi Liu, Qingyong Hu +1
The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-struc…
Model Inversion Attacks against Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang +3
Many data mining tasks rely on graphs to model relational structures among individuals (nodes). Since relational data are often sensitive, there is an urgent need to evaluate the p…
ProtGNN: Towards Self-Explaining Graph Neural Networks
Zaixi Zhang, Qi Liu, Hao Wang +2
Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions made by GNNs. Existing explanation methods mainly focus on post-hoc e…