8 citations · 17 across the 4 of their papers we have counts for
4 papers
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
Jialin Chen, Yuelin Wang, Cristian Bodnar +3
Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistin…
Generative Explanations for Graph Neural Network: Methods and Evaluations
Jialin Chen, Kenza Amara, Junchi Yu +1
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks. However, the black-box nature often limits their interpretability and trustworthin…
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Jialin Chen, Rex Ying
Temporal graphs are widely used to model dynamic systems with time-varying interactions. In real-world scenarios, the underlying mechanisms of generating future interactions in dyn…
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
Jialin Chen, Shirley Wu, Abhijit Gupta +1
The widespread deployment of Graph Neural Networks (GNNs) sparks significant interest in their explainability, which plays a vital role in model auditing and ensuring trustworthy g…