2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
Nucleus subtype classification using inter-modality learning
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +14
Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available b…
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…
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…